The errors young children make when recognising others’ emotions may be systematic over-identification biases and may partially explain the challenges some have socially. These biases and associations may be differential by emotion. In a sample of 871 ethnically and racially diverse preschool-aged children (i.e. 33–68 months; 49% Hispanic/Latine, 52% Children of Colour), emotion recognition was assessed, and scores for accuracy and bias were calculated by emotion (i.e. anger, sad, happy, calm, and fear). Child and family characteristics and teacher-reported (...) social behaviour were also collected. Multilevel structural equation modelling revealed emotion-specific recognition accuracies varied between 36 and 65% whereas biases varied between 4 and 13%. Anger was the strongest bias followed by sad, happy, fear, and calm, in contrast to the pattern for accuracy – happy, sad, angry, fear, and calm. More variance was explained in emotion-specific recognition accuracies by child and family characteristics – 7–38% – than biases – 3–7%. Negatively-valanced emotion recognition biases associated with positively-valanced accuracies, and positively-valued emotion recognition biases associated with negatively-valued accuracies. Biases did not have meaningful associations with social behaviour. This study highlights that children’s emotion recognition errors may partially be systematic, but future studies are needed to understand the underlying cognitive mechanisms. (shrink)

The value of accuracy and quantity as memory measures is assessed. It is argued that (1) accuracy does not measure correspondence (monitoring) because it ignores omissions and correct rejections, (2) quantity is confounded with monitoring in recall, and (3) in recognition, if targets and foils are unequal, both measures, even together, still ignore correct rejections.

The structure of the deep artificial neural network is similar to the structure of the biological neural network, which can be well applied to the 3D visual image recognition of aerobics movements. A lot of results have been achieved by applying deep neural networks to the 3D visual image recognition of aerobics movements, but there are still many problems to be overcome. After analyzing the expression characteristics of the convolutional neural network model for the three-dimensional visual image characteristics (...) of aerobics, this paper builds a convolutional neural network model. The model is improved on the basis of the traditional model and unifies the process of aerobics 3D visual image segmentation, target feature extraction, and target recognition. The convolutional neural network and the deep neural network based on autoencoder are designed and applied to aerobics action 3D visual image test set for recognition and comparison. We improve the accuracy of network recognition by adjusting the configuration parameters in the network model. The experimental results show that compared with other simple models, the model based on the improved AdaBoost algorithm can improve the final result significantly when the accuracy of each model is average. Therefore, the method can improve the recognition accuracy when multiple neural network models with general accuracy are obtained, thereby avoiding the complicated parameter adjustment process to obtain a single optimal network model. (shrink)

The conflicting findings from the few studies conducted with regard to gender differences in the recognition of vocal expressions of emotion have left the exact nature of these differences unclear. Several investigators have argued that a comprehensive understanding of gender differences in vocal emotion recognition can only be achieved by replicating these studies while accounting for influential factors such as stimulus type, gender-balanced samples, number of encoders, decoders, and emotional categories. This study aimed to account for these factors (...) by investigating whether emotion recognition from vocal expressions differs as a function of both listeners' and speakers' gender. A total of N = 290 participants were randomly and equally allocated to two groups. One group listened to words and pseudo-words, while the other group listened to sentences and affect bursts. Participants were asked to categorize the stimuli with respect to the expressed emotions in a fixed-choice response format. Overall, females were more accurate than males when decoding vocal emotions, however, when testing for specific emotions these differences were small in magnitude. Speakers' gender had a significant impact on how listeners' judged emotions from the voice. The group listening to words and pseudo-words had higher identification rates for emotions spoken by male than by female actors, whereas in the group listening to sentences and affect bursts the identification rates were higher when emotions were uttered by female than male actors. The mixed pattern for emotion-specific effects, however, indicates that, in the vocal channel, the reliability of emotion judgments is not systematically influenced by speakers' gender and the related stereotypes of emotional expressivity. Together, these results extend previous findings by showing effects of listeners' and speakers' gender on the recognition of vocal emotions. They stress the importance of distinguishing these factors to explain recognition ability in the processing of emotional prosody. (shrink)

There is debate within the literature as to whether emotion dysregulation (ED) in Attention-Deficit Hyperactivity Disorder (ADHD) reflects deviant attentional mechanisms or atypical perceptual emotion processing. Previous reviews have reliably examined the nature of facial, but not vocal, emotion recognition accuracy in ADHD. The present meta-analysis quantified vocal emotion recognition (VER) accuracy scores in ADHD and controls using robust variance estimation, gathered from 21 published and unpublished papers. Additional moderator analyses were carried out to determine whether (...) the nature of VER accuracy in ADHD varied depending on emotion type. Findings revealed a medium effect size for the presence of VER deficits in ADHD, and moderator analyses showed VER accuracy in ADHD did not differ due to emotion type. These results support the theories which implicate the role of attentional mechanisms in driving VER deficits in ADHD. However, there is insufficient data within the behavioural VER literature to support the presence of emotion processing atypicalities in ADHD. Future neuro-imaging research could explore the interaction between attention and emotion processing in ADHD, taking into consideration ADHD subtypes and comorbidities. (shrink)

The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that the more training samples (...) the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same. (shrink)

BackgroundWe review original papers on ovarian-hormone status in two areas of emotional processing: facial emotion recognition and emotional memory. Ovarian-hormone status is operationalized by the levels of the steroid sex hormones 17β-estradiol and progesterone, fluctuating over the natural menstrual cycle and suppressed under oral contraceptive use. We extend previous reviews addressing single areas of emotional processing. Moreover, we systematically examine the role of stimulus features such as emotion type or stimulus valence and aim at elucidating factors that reconcile the (...) inconsistent results.MethodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and included papers published until September 2020 indexed in PubMed and Web of Science databases. Search terms were MeSH terms AND AND with representing our separately searched areas, resulting in, and. To be included, articles had to be written and published in English, examine healthy, non-pregnant adult women in their reproductive age, and measure or at least estimate levels of E2 and P4. In PubMed, the search was limited to humans and to the search term present in the title or abstract.ResultsFeatures of the provided stimulus material constitute a relevant influence that interacts with E2- and P4-related ovarian-hormone status. For instance, recognition of basic emotions appears to be more related to P4- than E2-levels. Quite consistent, OC intake was accompanied by impaired recognition accuracy of basic and also complex emotions, although not in a recent large-sample study assessing complex emotions. Memory recall of negative content was mainly enhanced by P4, especially after having been stressed.Discussion and ConclusionWe document the methodological diversity in the field, presumably contributing to the heterogeneity of results. More studies explicitly contrasting the early follicular phase, mid-cycle phase, mid-luteal, and OC intake while standardizing tasks are needed. Research would take advantage of using within-subject designs and accounting for the recognition of complex emotions. (shrink)

In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then (...) extracted from each channel. Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods. (shrink)

Linguistic communication is an important part of the cross-cultural perspective, and linguistic textual emotion recognition is a key massage in interpersonal communication. Spanish is the second largest language system in the world. The purpose of this paper is to identify the emotional features in Spanish texts. The improved BiLSTM framework is proposed. We select three widely used Spanish dictionaries as the datasets for our experiments, and then we finally obtain text sentiment classification results through text preprocessing, text emotion feature (...) extraction, text topic detection, and emotion classification. We inserted the attention mechanism in the improved BiLSTM framework. It enables the shared feature encoder to obtain weighted representation results in the extraction of emotion features, which enhances the generalization ability of the model for text emotion feature recognition. Experimental results demonstrate that our approach performs better for specialized Spanish dictionary datasets. In terms of emotion recognition accuracy, the average value is as high as 76.21%. The overall performance outperforms current comparable machine learning methods and convolutional neural network methods. (shrink)

As a branch of statistical latent variable modeling, multidimensional item response theory plays an important role in psychometrics. Multidimensional graded response model is a key model for the development of multidimensional computerized adaptive testing with graded-response data and multiple traits. This paper explores how to automatically identify the item-trait patterns of replenished items based on the MGRM in MCAT. The problem is solved by developing an exploratory pattern recognition method for graded-response items based on the least absolute shrinkage and (...) selection operator, which is named LPRM-GR and facilitates the subsequent parameter estimation of replenished items and helps maintaining the effectiveness of item replenishment in MCAT. In conjunction with the proposed approach, the regular BIC and weighted BIC are applied, respectively, to select the optimal item-trait patterns. Simulation for evaluating the LPRM-GR in pattern recognition accuracy of replenished items and the corresponding item estimation accuracy is conducted under multiple conditions across different numbers with respect to dimensionality, response-category numbers, latent trait correlation, stopping rules, and item selection criteria. Results show that the proposed method with the two types of BIC both have good performance in pattern recognition for item replenishment in the two- to four-dimensional MCAT with the MGRM, for which the weighted BIC is generally superior to the regular BIC. The proposed method has relatively high accuracy and efficiency in identifying the patterns of graded-response items, and has the advantages of easy implementation and practical feasibility. (shrink)

The present study examines the association between people’s interoceptive representation of physical sensations and the recognition of vocal and facial expressions of emotion. We used body maps to study the granularity of the interoceptive conceptualisation of 11 positive emotions (amusement, awe, compassion, contentment, desire, love, joy, interest, pride, relief, and triumph) and a new emotion recognition test (Emotion Expression Understanding Test) to assess the ability to recognise emotions from vocal and facial behaviour. Overall, we found evidence for distinct (...) interoceptive conceptualizations of 11 positive emotions across Asian American, European American, and Latino/a American cultures, as well as the reliable identification of emotion in facial and vocal expressions. Central to new theorising about emotion-related representation, the granularity of physical sensations did not covary with emotion recognition accuracy, suggesting that two kinds of emotion conceptualisation processes might be distinct. (shrink)

Current methods of chaos-based action recognition in videos are limited to the artificial feature causing the low recognition accuracy. In this paper, we improve ChaosNet to the deep neural network and apply it to action recognition. First, we extend ChaosNet to deep ChaosNet for extracting action features. Then, we send the features to the low-level LSTM encoder and high-level LSTM encoder for obtaining low-level coding output and high-level coding results, respectively. The agent is a behavior recognizer (...) for producing recognition results. The manager is a hidden layer, responsible for giving behavioral segmentation targets at the high level. Our experiments are executed on two standard action datasets: UCF101 and HMDB51. The experimental results show that the proposed algorithm outperforms the state of the art. (shrink)

Micro-expressions, as fleeting facial expressions, are very important for judging people’s true emotions, thus can provide an essential behavioral clue for lie and dangerous demeanor detection. From embodied accounts of cognition, we derived a novel hypothesis that facial feedback from upper and lower facial regions has differential effects on micro-expression recognition. This hypothesis was tested and supported across three studies. Specifically, the results of Study 1 showed that people became better judges of intense micro-expressions with a duration of 450 (...) ms when the facial feedback from upper face was enhanced via a restricting gel. Additional results of Study 2 showed that the recognition accuracy of subtle micro-expressions was significantly impaired under all duration conditions (50 ms, 150 ms, 333 ms, and 450 ms) when facial feedback from lower face was enhanced. In addition, the results of Study 3 also revealed that blocking the facial feedback of lower face, significantly boosted the recognition accuracy of subtle and intense micro-expressions under all duration conditions (150 ms and 450 ms). Together, these results highlight the role of facial feedback in judging the subtle movements of micro-expressions. (shrink)

Previous work has shown that children with dyslexia are impaired in speech recognition in adverse listening conditions. Our study further examined how semantic context and fundamental frequency (F0) contours contribute to word recognition against interfering speech in dyslexic and non-dyslexic children. Thirty-two children with dyslexia and 35 chronological-age-matched control children were tested on the recognition of words in normal sentences versus wordlist sentences with natural versus flatF0contours against single-talker interference. The dyslexic children had overall poorer recognition (...) performance than non-dyslexic children. Furthermore, semantic context differentially modulated the effect ofF0contours on the recognition performances of the two groups. Specifically, compared with flatF0contours, naturalF0contours increased the recognition accuracy of dyslexic children less than non-dyslexic children in the wordlist condition. By contrast, naturalF0contours increased the recognition accuracy of both groups to a similar extent in the sentence condition. These results indicate that access to semantic context improves the effect of naturalF0contours on word recognition in adverse listening conditions by dyslexic children who are more impaired in the use of naturalF0contours during isolated and unrelated word recognition. Our findings have practical implications for communication with dyslexic children when listening conditions are unfavorable. (shrink)

Difficulties understanding speech form one of the most prevalent complaints among older adults. Successful speech perception depends on top-down linguistic and cognitive processes that interact with the bottom-up sensory processing of the incoming acoustic information. The relative roles of these processes in age-related difficulties in speech perception, especially when listening conditions are not ideal, are still unclear. In the current study, we asked whether older adults with a larger working memory capacity process speech more efficiently than peers with lower capacity (...) when speech is presented in noise, with another task performed in tandem. Using the Eye-tracking of Word Identification in Noise Under Memory Increased Load an adapted version of the “visual world” paradigm, 36 older listeners were asked to follow spoken instructions presented in background noise, while retaining digits for later recall under low or high memory load. In critical trials, instructions directed listeners’ gaze to pictures of objects whose names shared onset or offset sounds with the name of a competitor that was displayed on the screen at the same time. We compared listeners with different memory capacities on the time course for spoken word recognition under the two memory loads by testing eye-fixations on a named object, relative to fixations on an object whose name shared phonology with the named object. Results indicated two trends. For older adults with lower working memory capacity, increased memory load did not affect online speech processing, however, it impaired offline word recognition accuracy. The reverse pattern was observed for older adults with higher working memory capacity: increased task difficulty significantly decreases online speech processing efficiency but had no effect on offline word recognition accuracy. Results suggest that in older adults, adaptation to adverse listening conditions is at least partially supported by cognitive reserve. Therefore, additional cognitive capacity may lead to greater resilience of older listeners to adverse listening conditions. The differential effects documented by eye movements and accuracy highlight the importance of using both online and offline measures of speech processing to explore age-related changes in speech perception. (shrink)

Haptic object recognition is usually an efficient process although slower and less accurate than its visual counterpart. The early loss of vision imposes a greater reliance on haptic perception for recognition compared to the sighted. Therefore, we may expect that congenitally blind persons could recognize objects through touch more quickly and accurately than late blind or sighted people. However, the literature provided mixed results. Furthermore, most of the studies on haptic object recognition focused on performance, devoting little (...) attention to the exploration procedures that conducted to that performance. In this study, we used iCube, an instrumented cube recording its orientation in space as well as the location of the points of contact on its faces. Three groups of congenitally blind, late blind and age and gender-matched blindfolded sighted participants were asked to explore the cube faces where little pins were positioned in varying number. Participants were required to explore the cube twice, reporting whether the cube was the same or it differed in pins disposition. Results showed that recognition accuracy was not modulated by the level of visual ability. However, congenitally blind touched more cells simultaneously while exploring the faces and changed more the pattern of touched cells from one recording sample to the next than late blind and sighted. Furthermore, the number of simultaneously touched cells negatively correlated with exploration duration. These findings indicate that early blindness shapes haptic exploration of objects that can be held in hands. (shrink)

This article reports on developing and evaluating a generic Handwritten Text Recognition (HTR) model created for the automatic computer-assisted transcription of Ukrainian handwriting publicly available via the HTR platform Transkribus. The model’s training process encompasses diverse datasets, including historical manuscripts by renowned poets Taras Shevchenko and Lesya Ukrainka, along with private correspondence used for the General Regionally Annotated Corpus of Ukrainian (GRAC) and a diary procured at the Holodomor Museum collection. We evaluate the model’s performance by comparing its theoretical (...) accuracy, with a character error rate (CER) of 4.2%, against its practical efficacy when augmented with an AI-based language model for Ukrainian and a Large Language Model. The model is versatile and functional and can thus be applied for mass-digitization of Ukrainian cultural heritage. In our outlook section, we identify possibilities for further improving the model. (shrink)

The recognition in electroencephalogram of action idea is to identify what action people want to do by EEG. The significance of this project is to help people who have trouble in movement. Their action ideas are identified by EEG, and then robot hands can assist them to complete the action. This paper, with comparative experiments, used OpenBCI to collect EEG action ideas during static action and dynamic action and used the EEG recognition model Conv1D-GRU to training and (...) class='Hi'>recognition action, respectively. The experimental result shows that the brain wave action idea is easier to recognize in static state. The accuracy of brain wave action idea recognition in dynamic state is only 72.27%, and the accuracy of brain wave action idea recognition in static state is 99.98%. The experimental result confirms that the action idea will be of great help to people with mobility difficulties. (shrink)

This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired (...) motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods. (shrink)

Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous (...) centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s. (shrink)

To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern and stacked autoencoder is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of (...) the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm; it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs. (shrink)

Based on evidence that learning new characters through handwriting leads to better recognition than learning through typing, some authors proposed that the graphic motor plans acquired through handwriting contribute to recognition. More recently two alternative explanations have been put forward. First, the advantage of handwriting could be due to the perceptual variability that it provides during learning. Second, a recent study suggests that detailed visual analysis might be the source of the advantage of handwriting over typing. Indeed, in (...) that study, handwriting and composition –a method requiring a detailed visual analysis but no specific graphomotor activity– led to equivalent recognition accuracy, both higher than typing. The aim of the present study was to assess whether the contribution of detailed visual analysis is observed in preschool children and to test the variability hypothesis. To that purpose, three groups of preschool children learned new symbols either by handwriting, typing, or composition. After learning, children performed first a four-alternative recognition task and then a categorization task. The same pattern of results as the one observed in adults emerged in the four-alternative recognition task, confirming the importance of the detailed visual analysis in letter-like shape learning. In addition, results failed to reveal any difference across learning methods in the categorization task. The latter results provide no evidence for the variability hypothesis which would predict better categorization after handwriting than after typing or composition. (shrink)

In the human-computer interaction, electroencephalogram access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy features of each frequency band were extracted. Secondly, the constructed 3D (...) MSE feature matrices were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network and hidden Markov models. Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method. (shrink)

Facial recognition is an artificial intelligence-based technology that, like many other forms of artificial intelligence, suffers from an accuracy deficit. This paper focuses on one particular use of facial recognition, namely identification, both as authentication and as recognition. Despite technological advances, facial recognition technology can still produce erroneous identifications. This paper addresses algorithmic identification failures from an upstream perspective by identifying the main causes of misidentifications (in particular, the probabilistic character of this technology, its ‘black (...) box’ nature and its algorithmic bias) and from a downstream perspective, highlighting the possible legal consequences of such failures in various scenarios (namely liability lawsuits). In addition to presenting the causes and effects of such errors, the paper also presents measures that can be deployed to reduce errors and avoid liabilities. (shrink)

A new three-order piecewise function was used to smoothen the model of Support Vector Machine( SVM) and a Third-order Piecewise Smooth SVM( TPSSVM) was proposed. By theory analyzing, approximation accuracy of the smooth function to the plus function is higher than that of the available. When dealing with the multi-class problem, a coding method of multi-class classification based on one-against-rest was proposed. Principal Component Analysis( PCA) was employed to extract the main features of face image set, and multi-class classification (...) of smooth SVM was used for face recognition. The experimental results on ORL and FERET face databases show that the recognition rate of smooth SVM for multi-class classification is better than the traditional identification methods. (shrink)

Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side-scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, (...) diversity, multisources, and small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K-means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images. (shrink)

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography -based brain-computer interface research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (...) and power spectral density, which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor, support vector machine, and random forest. We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. (shrink)

A role of perirhinal cortex (PrC) in recognition memory for objects has been well established. Contributions of parahippocampal cortex (PhC) to this function, while documented, remain less well understood. Here, we used fMRI to examine whether the organization of item-based recognition memory signals across these two structures is shaped by object category, independent of any difference in representing episodic context. Guided by research suggesting that PhC plays a critical role in processing landmarks, we focused on three categories of (...) objects that differ from each other in their landmark suitability as confirmed with behavioral ratings (buildings > trees > aircraft). Participants made item-based recognition-memory decisions for novel and previously studied objects from these categories, which were matched in accuracy. Multi-voxel pattern classification revealed category-specific item-recognition memory signals along the long axis of PrC and PhC, with no sharp functional boundaries between these structures. Memory signals for buildings were observed in the mid to posterior extent of PhC, signals for trees in anterior to posterior segments of PhC, and signals for aircraft in mid to posterior aspects of PrC and the anterior extent of PhC. Notably, item-based memory signals for the category with highest landmark suitability ratings were observed only in those posterior segments of PhC that also allowed for classification of landmark suitability of objects when memory status was held constant. These findings provide new evidence in support of the notion that item-based memory signals for objects are not limited to PrC, and that the organization of these signals along the longitudinal axis that crosses PrC and PhC can be captured with reference to landmark suitability. (shrink)

This study explored the priming effects of discriminated experiences on emotion recognition accuracy of Asian Americans. We hypothesized that when Asian Americans were reminded of discriminated experiences due to their race, they would detect subtle negative emotional expressions on White faces more accurately than would Asian Americans who were primed with a neutral topic. This priming effect was not expected to emerge in detecting negative facial expressions on Asian faces. To test this hypothesis, 108 participants were randomly assigned (...) to one of two conditions: write about their experiences with racial discrimination or write about a neutral topic. Then, they were given an emotion recognition test consisting of White and Asian faces. The current study found a significant interaction effect of priming condition by target race. When Asian Americans were reminded of discriminated experiences, they displayed heightened sensitivity to negative emotional expressions on White faces, but not to the negative expressions on Asian faces. The implications of these findings were discussed. (shrink)

Emotion recognition is an important part of building an intelligent human-computer interaction system and plays an important role in human-computer interaction. Often, people express their feelings through a variety of symbols, such as words and facial expressions. A business ecosystem is an economic community based on interacting organizations and individuals. Over time, they develop their capabilities and roles together and tend to develop themselves in the direction of one or more central enterprises. This paper aims to study a multimodal (...) ER method based on attention mechanism. It analyzes the current emotional state of consumers and the development direction of enterprises through multi-modal ER of human emotions and analysis of market trends, so as to provide the most appropriate response or plan. This paper firstly describes the related methods of multimodal ER and deep learning in detail, and briefly outlines the meaning of enterprise strategy in the business ecosystem. Then, two datasets, CMU-MOSI and CMU-MOSEI, are selected to design the scheme for multimodal ER based on self-attention mechanism. Through the comparative analysis of the accuracy of single-modal and multi-modal ER, the self-attention mechanism is applied in the experiment. The experimental results show that the average recognition accuracy of happy under multimodal ER reaches 91.5%. (shrink)

Vehicle type recognition algorithms are broadly used in intelligent transportation, but the accuracy of the algorithms cannot meet the requirements of production application. For the high efficiency of the multilayer perceptive layer of Network in Network, the nonlinear features of local receptive field images can be extracted. Global average pooling can avoid the network from overfitting, and small convolution kernel can decrease the dimensionality of the feature map, as well as downregulate the number of model training parameters. On (...) that basis, the residual error is adopted to build a novel NIN model by altering the size and layout of the original convolution kernel of NIN. The feasibility of the algorithm is verified based on the Stanford Cars dataset. By properly setting weights and learning rates, the accuracy of the NIN model for vehicle type recognition reaches 97.2%. (shrink)

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; (...) Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions. (shrink)

Speech recognition plays an important role in human–computer interaction. The higher the accuracy and efficiency of speech recognition are, the larger the improvement of human–computer interaction performance. This article briefly introduced the hidden Markov model -based English speech recognition algorithm and combined it with a back-propagation neural network to further improve the recognition accuracy and reduce the recognition time of English speech. Then, the BPNN-combined HMM algorithm was simulated and compared with the HMM (...) algorithm and the BPNN algorithm. The results showed that increasing the number of test samples increased the word error rate and recognition time of the three speech recognition algorithms, among which the word error rate and recognition time of the BPNN-combined HMM algorithm were the lowest. In conclusion, the BPNN-combined HMM can effectively recognize English speeches, which provides a valid reference for intelligent recognition of English speeches by computers. (shrink)

Fingerprint recognition is the most widely used identification method at present. However, it still falls short in terms of cross-platform and algorithmic complexity, which exerts a certain effect on the migration of fingerprint data and the development of the system. The conventional image recognition methods require offline standard databases constructed in advance for image access efficiency. The database can provide a pre-processed image via a specific method that probably is compatible merely with the specific recognition algorithm. Then, (...) the specific recognition algorithm starts the process of retrieving these specific pre-proessing images for recognition and inevitably will be blocked from other datasets. The proposed method in this research designed an embedded image processing algorithm based on a Siamese neural network in the recognition method that allows the proposed method to recognize images from any source without constructing a database for image storage in advance. In this research, the proposed method was applied to fingerprint recognition and evaluation of the proposed method was evaluated. The results showed that the accuracy of the proposed algorithm was up to 92%, and its F1 score was up to 0.87. Compared with the conventional fingerprint matching methods, its significant advantage in the FRR, FAR, and CR jointly indicated the remarkable correct recognition rate of the proposed method. (shrink)

Self–other discrimination was investigated with point-light displays in which actions were presented with or without additional auditory information. Participants first executed different actions in time with music. In two subsequent experiments, they watched point-light displays of their own or another participant’s recorded actions, and were asked to identify the agent . Manipulations were applied to the visual information and to the auditory information . Results indicate that self-recognition was better than chance in all conditions and was highest when observing (...) relatively unconstrained patterns of movement. Auditory information did not increase accuracy even with the most ambiguous visual displays, suggesting that judgments of agent identity depend much more on motor cues than on auditory or audiovisual information. (shrink)

There are complex posture changes in dance movements, which lead to the low accuracy of dance movement recognition. And none of the current motion recognition uses the dancer’s attributes. The attribute feature of dancer is the important high-level semantic information in the action recognition. Therefore, a dance movement recognition algorithm based on feature expression and attribute mining is designed to learn the complicated and changeable dancer movements. Firstly, the original image information is compressed by the (...) time-domain fusion module, and the information of action and attitude can be expressed completely. Then, a two-way feature extraction network is designed, which extracts the details of the actions along the way and takes the sequence image as the input of the network. Then, in order to enhance the expression ability of attribute features, a multibranch spatial channel attention integration module based on an attention mechanism is designed to extract the features of each attribute. Finally, using the semantic inference and information transfer function of the graph convolution network, the relationship between attribute features and dancer features can be mined and deduced, and more expressive action features can be obtained; thus, high-performance dance motion recognition is realized. The test and analysis results on the data set show that the algorithm can recognize the dance movement and improve the accuracy of the dance movement recognition effectively, thus realizing the movement correction function of the dancer. (shrink)

Fast and accurate recognition of teammates is crucial in contexts as varied as fast-moving sports, the military, and law enforcement engagements; misrecognition can result in lost scoring opportunities in sport or friendly fire in combat contexts. Initial studies on teammate recognition in sport suggests that athletes are adept at this perceptual ability but still susceptible to errors. The purpose of the current proof-of-concept study was to explore the trainability of teammate recognition from very brief exposure to vision (...) of the whole-body form and motion of a previously unknown individual. Participants were divided into three groups: a 4-week training group who were also the actors for the test and training footage, a 2-week training group, and a no-training group. Findings revealed significant differences between the training groups and their improvement from the pre-to post-test on Response Accuracy and Movement Time. The current study found the best performance in the 4-week Training group. The biggest improvement was found in the 2-week training group, whilst no significant improvement was made in the Control group. These results suggest that training was effective, but also indicate that having initially performed the movements as actors may have led to improvements in baseline testing and ultimately the best results, thus physical performance of skills combined with video-based training may reduce the amount of time needed to improve teammate identification. (shrink)