Research on emotion recognition method of weightlifters based on a non-negative matrix decomposition algorithm

In order to overcome the problem of high recognition error rate in traditional emotional tendency recognition methods, a Mandarin emotional tendency recognition method based on expression spatiotemporal feature recognition is proposed. This method extracts the spatiotemporal features of the expression of the research object, and uses the data fusion technology to fuse the extracted feature vector. This paper constructs the standard database of Mandarin emotional tendency recognition, and takes the database as the standard of emotional tendency recognition. The fusion feature vector is matched with the standard feature in the database to get the result of Mandarin emotional tendency recognition. The experimental results show that compared with the traditional method for Mandarin emotional tendency recognition, the recognition method based on the spatiotemporal feature of expression can reduce the recognition error by about 75%.

Emotion recognition using cross-modal attention from EEG and facial expression

A novel facial emotion recognition method for stress inference of facial nerve paralysis patients

In this paper, we present a new method for emotion recognition from facial expressions. The proposed algorithm concentrates on only two specific areas (eyes and mouth), reducing features and descriptors and focusing only on these areas. The algorithm extracts characteristics from these two regions of the face and, in a subsequent process, eliminates the less significant characteristics or those that introduce noise into the classifier. The system allows obtaining a reduced set of features to improve the performance of the classification. In the experiments carried out, we obtain precisions of 99.56%. We evaluated the proposed algorithm on two benchmark datasets; we find that SVM consistently outperforms traditional machine learning techniques.

A survey of emotion recognition methods with emphasis on E-Learning environments

Emotion recognition from EEG based on multi-task learning with capsule network and attention mechanism

Patients with amygdala lesions were found to be impaired in recognizing the fear emotion both from face and from music. In patients with Parkinson's disease (PD), impairment in recognition of emotions from facial expressions was reported for disgust, fear, sadness and anger, but no studies had yet investigated this population for the recognition of emotions from both face and music. The ability to recognize basic universal emotions (fear, happiness and sadness) from both face and music was investigated in 24 medicated patients with PD and 24 healthy controls. The patient group was tested for language (verbal fluency tasks), memory (digit and spatial span), executive functions (Similarities and Picture Completion subtests of the WAIS III, Brixton and Stroop tests), visual attention (Bells test), and fulfilled self-assessment tests for anxiety and depression. Results showed that the PD group was significantly impaired for recognition of both fear and sadness emotions from facial expressions, whereas their performance in recognition of emotions from musical excerpts was not different from that of the control group. The scores of fear and sadness recognition from faces were neither correlated to scores in tests for executive and cognitive functions, nor to scores in self-assessment scales. We attributed the observed dissociation to the modality (visual vs. auditory) of presentation and to the ecological value of the musical stimuli that we used. We discuss the relevance of our findings for the care of patients with PD.

Technology-assisted intervention has the potential to adaptively individualize and improve outcomes of traditional schizophrenia SZ intervention. Virtual reality VR technology, in particular, has the potential to simulate real world social and communication interactions and hence could be useful as a therapeutic platform for SZ. Emotional face recognition is considered among the core building blocks of social communication. Studies have shown that emotional face processing and understanding is impaired in patients with SZ. The current study develops a novel VR-based system that presents avatars that can change their facial emotion dynamically for emotion recognition tasks. Additionally, this system allows real-time measurement of physiological signals and eye gaze during the emotion recognition tasks, which can be used to gain insight about the emotion recognition process in SZ population. This study further compares VR-based facial emotion recognition with that of the more traditional emotion recognition from static faces using a small usability study. Results from the usability study suggest that VR could be a viable platform for SZ intervention and implicit signals such as physiological signals and eye gaze can be utilized to better understand the underlying pattern that is not available from user reports and performance alone.

In this work, we have proposed STLEV (Siamese Neural Network with Triplet Loss and Long Short-Term Memory for Emotion recognition in Videos) a method for emotion recognition in videos. This method is beneficial for cognitive Human-Computer interaction. Emotion recognition from video is a challenging task as only few frames of a video will contain relevant information for recognizing emotions. Also identifying the apex frame is challenging. Emotion recognition from videos involves extracting the features from the frames of a video and classifying the sequence of features. For feature extraction we have used Siamese Neural Network (SNN) which is metric based meta-learning model. SNN is trained with triplet loss. For classification, Long Shot-Term Memory (LSTM) model is used. In this work we have used spatial and temporal aspects of video. Spatial features are extracted using SNN and temporal features using LSTM. Deep learning models need large amount of data for good accuracy. We have addressed this problem by leveraging few shot learning aspects of metric meta-learning so that fewer samples are needed for training the model used for feature extraction. Experiments done using BU-4DFE dataset achieved an accuracy of 87.5% which demonstrates the efficiency of the method STLEV.

Every human has emotions in life at all phases of life’s moment or instance. These emotions keep changing ever so often and person to person because of the biological reflexes of human brain produced by the nervous system due to the neurophysiological changes which are closely associated with behavior, mood swings; thoughts, etc. are tending to be reflected as an expression on the human face. The application of study of these emotions of humans using facial expressions became very prominent in the fields of biomedical engineering, neuroscience, product screening, marketing, and advertisement, etc. In early works of emotion recognition is for the most part useful for analyzing product reviews or some methodologies by monitoring videos and images in real time. Imparting artificial intelligence to the future of the world where every industry thrives well. In recent years, progressive growth in artificial intelligence is seen especially in the field of machine learning with the predominant subset called deep learning. Mainly, deep learning algorithms made an overriding burgeon in image classification thereby avoiding the complex process in facial recognition. The framework proposed in this paper describes conceptual and theoretical knowledge for human facial emotion recognition with a labeled model and applying a Haar cascade classifier using CNN classification—a class in deep neural networks used in its implementation.KeywordsImage PreprocessingFacial Emotion RecognitionHaar Cascade ClassifierData AugmentationDeep LearningCNN—Convolutional Neural NetworkAdaboost TrainingPrediction

Humans have the ability to perceive and depict a wide range of emotions. There are various models that can recognize seven primary emotions from facial expressions (joyful, gloomy, annoyed, dreadful, wonder, antipathy, and impartial). This can be accomplished by observing various activities such as facial muscle movements, speech, hand gestures, and so forth. Automatic emotion recognition is a significant issue that has been a hotly debated research topic in recent years. At the moment, several research people have taken a component in inheriting or extra multimodal for higher understanding. This paper indicates a method for emotion recognition that makes use of 3 modalities: facial images, audio indicators, and text detection from FER and CK+, RAVDESS, and Twitter tweets datasets, respectively. The CNN model achieved 66.67 percent on the FER-2013 dataset of labeled headshots while on the CK+ dataset, 98.4 percent accuracy was obtained. Finally, diverse fusion strategies had been approached, and each of those fusion techniques gave distinctive results. This project is a step towards the sense of interaction between human emotional aspects and the growing technology that is the future of development in today’s world.

Emotion recognition has become an important research area for advanced human-robot interaction. Through recognizing facial expressions, a robot can interact with a person with a more friendly manner. In this paper, we proposed a bimodal emotion recognition system by combining image and speech information. A novel information fusion strategy is proposed to set proper weights to two feature modalities based on their recognition reliability. The fusion weights are determined by the distance between test data and the classification hyperplane and the standard deviation of training samples. After normalization using the mean distance between training samples and the hyperplane, the fusion weight is set to represent the classification reliability of individual modality. In the latter bimodal SVM classification, the recognition result with higher weight is selected. The complete procedure has been implemented in a DSP-based system to recognize five facial expressions on-line in real time. The experimental results show that a recognition rate of 86.9% is achieved, an improvement of 5% compared with using only image information.

In recent years, affective computing has become a research hotspot in the area of natural language processing and Chinese emotion recognition is an important constituent. This paper proposes a method of Chinese emotion recognition based on three-way decisions. Given the emotion dictionary constructed firstly, the grammatical information of sentences, topic features of texts and three-way decisions are integrated and applied into Chinese emotion recognition, thus realizing the multi-label emotion recognition of sentences in Chinese texts. The results of experiments show that the method of Chinese emotion recognition, based on three-way decisions, has achieved excellent results in the emotion recognition of Chinese sentences.

Emotional state recognition is an important component for efficient human-computer interaction. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. The general 3D based methods only consider geometric information for feature extraction. In this paper, we present a real 3D visual features based method for human emotion recognition. 3D geometric information plus colour/density information of the facial expressions are extracted by 3D Gabor library to construct visual feature vectors. The filter's scale, orientation, and shape of the library are specified according to the appearance patterns of the 3D facial expressions. An improved kernel canonical correlation analysis (IKCCA) algorithm is proposed for final decision. From training samples, the semantic ratings that describe the different facial expressions are computed by IKCCA to generate a seven dimensional semantic expression vector. It is applied for learning the correlation with different testing samples. According to this correlation, we estimate the associated expression vector and perform expression classification. From experiment results, our proposed method demonstrates impressive performance.

IntroductionRecent developments in the field of social cognition have led to a renewed interest in basic and social emotion recognition in early stages of Alzheimer's Disease (AD) and FrontoTemporal Dementia (FTD). Despite the growing attention to this issue, only few studies have attempted to investigate emotion recognition using both visual and vocal stimuli. In addition, recent studies have presented conflicting findings regarding the extent of impairment in patients in the early stages of these diseases. The present study aims to investigate emotion understanding (both basic and social emotions), using different tasks with visual and auditory stimuli, to identify supramodal deficits in AD and FTD to provide a reliable tool to better outline their behavioral and emotional profile and useful instruments for their management.MethodsEighteen patients with AD and 15 patients with FTD were included in the study. Healthy control (HCs) subjects were recruited to obtain normative data for basic emotion recognition tests and social emotion recognition tasks. To evaluate basic emotion recognition, the Facial Emotion Recognition Battery (FERB) and the Emotional Prosody Recognition Battery (EPRB) were administered. To evaluate social emotion recognition, the Faux Pas (FP), Reading the Mind in the Eyes (RME), and Reading the Mind in the Voice (RMV) tests were employed.ResultsFTD patients performed significantly worse than HCs in most of the subtests of the basic emotion recognition batteries, where, instead, AD patients were significantly impaired only when required to match emotional facial expression in different individuals (subtask of the FERB). Moreover, FTD patients scored significantly lower in RME and RMV tests compared both to AD patients and to HCs. In addition, ADs were selectively impaired in RMV as respect to HCs.DiscussionFTD patients showed deficits in emotion recognition, affecting both basic and social emotions, whether conveyed through facial expressions or prosody. This result may explain the well-known social behavioral difficulties observed in FTD patients from the early stages of the disease. The fewer and specific deficits in AD patients with comparable MMSE scores may be attributed to the mild degree of impairment, as these deficits may appear later in the progression of AD.