Facial decomposition for expression recognition using texture/shape descriptors and SVM classifier

Objectives: Attention-deficit/hyperactivity disorder (ADHD) is the most frequent neurobehavioural disorder in childhood. ADHD is associated with impaired academic performance, cognitive, and emotional deficits. Moreover, comorbid oppositional defiant disorder (ODD) is leading to more severe impairment in social performance. Social cognition involves recognition, encoding, and interpretation of emotions from faces. Basic facial expressions that include sadness, happiness, anger, disgust, fear, and surprise are the easiest emotions to recognize. We aimed to demonstrate facial expression recognition impairments that might occur more frequently in children with co-occurring ADHD/ODD than patients with ADHD only. Thus, children with the co-occurrence of ODD may suffer more severely from social and behavioural difficulties.Methods: Forty patients diagnosed with ADHD and/or co-occurring ADHD/ODD according to DSM-IV-TR criteria were compared with a parallel (by gender, age, and educational state) 11 healthy children as a control group in this study. Clear facial images of each emotion were used as well as two additional sets of photos include 50% blurred images and cropped eye images were added as distractors then all images represented with black and white tone for emotion recognition task via facial expression. Angry expressions presented as target expressions. DSM-IV-Based Screening and Rating Scale for Children and Adolescents with attention deficit and disruptive behaviour disorders, the Conners’ Teachers Rating Scale/Revised Long Form and the Conners’ Parent Rating Scale/Revised Long Form were used to provide diagnostic objectivity.Results: Control group statistically performed better than ADHD group on recognition of emotional facial expressions. Results showed no statistically significant differences between the ADHD and ADHD/ODD group on recognition of emotional facial expressions. However, according to results of emotion recognition task via facial expressions, there were statistically significant differences between pure ADHD and comorbid ADHD+ODD groups in happy and neutral expressions. ADHD/ODD group tend to attribute more meaning to neutral facial expressions. Additionally there was statistically significant difference between control group and ADHD group according to recognition of angry expressions. There were statistically significant differences between the groups according to recognition of sad expressions in all clear, blurred, and eye photographs.Conclusions: Difficulties in recognizing emotional facial expressions were observed in children with ADHD. A statistically significant association was established between presence of ADHD and impaired recognition of facial emotion expressions independent from the scores of the disruptive behaviour rating scale. Comorbid ODD was not associated with recognition of emotional facial expressions including angry expressions. Recognition of angry expressions was not found as a predictor for disruptive behaviour disorders.

We proposed a facial motion tracking and expression recognition system based on video data. By a 3D deformable facial model, the online statistical model (OSM) and cylinder head model (CHM) were combined to track 3D facial motion in the framework of particle filtering. For facial expression recognition, a fast and efficient algorithm and a robust and precise algorithm were developed. With the first, facial animation and facial expression were retrieved sequentially. After that facial animation was obtained, facial expression was recognized by static facial expression knowledge learned from anatomical analysis. With the second, facial animation and facial expression were simultaneously retrieved to increase the reliability and robustness with noisy input data. Facial expression was recognized by fusing static and dynamic facial expression knowledge, the latter of which was learned by training a multi-class expressional Markov process using a video database. The experiments showed that facial motion tracking by OSM+CHM is more pose robust than that by OSM, and the facial expression score of the robust and precise algorithm is higher than those of other state-of-the-art facial expression recognition methods.

Facial expressions recognition and synthesis are important research fields to study how human beings reflect to environments in affective computing. With the rapid development of mathematical theory on multivariate statistics and multi-media technology especially image processing, facial expressions recognition researchers have achieved many useful results. Recently studies show that approaches to facial modeling and expressions recognition and synthesis analysis could be adapted to control security or even real-time health monitoring in the real world. Similarity, how to achieve facial expressions recognition and synthesis for independent-free mechanism is a central design in general. Based on the cognitive analysis of independent user's affective facial recognition researches we proposed an emotions composition model to dope out what are the user's new affective facial expressions. At this point, principal component, cluster and discriminate analysis were applied to show and verify independent user's affective expressions can be composited for synthesis by basic facial expressions such as happy, neutral and unhappy. Experiments were conducted to prove our models to be very significant.

Background: Individuals with child maltreatment (CM) experiences show alterations in emotion recognition (ER). However, previous research has mainly focused on populations with specific mental disorders, which makes it unclear whether alterations in the recognition of facial expressions are related to CM, to the presence of mental disorders or to the combination of CM and mental disorders, and on ER of emotional, rather than neutral facial expressions. Moreover, commonly, recognition of static stimulus material was researched. Objective: We assessed recognition of dynamic (closer to real life) negative, positive and neutral facial expressions in individuals characterised by CM, rather than a specific mental disorder. Moreover, we assessed whether they show a negativity bias for neutral facial expressions and whether the presence of one or more mental disorders affects recognition. Methods: Ninety-eight adults with CM experiences (CM+) and 60 non-maltreated (CM−) adult controls watched 200 non-manipulated coloured video sequences, showing 20 neutral and 180 emotional facial expressions, and indicated whether they interpreted each expression as neutral or as one of eight emotions. Results: The CM+ showed significantly lower scores in the recognition of positive, negative and neutral facial expressions than the CM− group (p < .050). Furthermore, the CM+ group showed a negativity bias for neutral facial expressions (p < .001). When accounting for mental disorders, significant effects stayed consistent, except for the recognition of positive facial expressions: individuals from the CM+ group with but not without mental disorder scored lower than controls without mental disorder. Conclusions: CM might have long-lasting influences on the ER abilities of those affected. Future research should explore possible effects of ER alterations on everyday life, including implications of the negativity bias for neutral facial expressions on emotional wellbeing and relationship satisfaction, providing a basis for interventions that improve social functioning.

With the work and study pressure on people increasing and the importance of mental health problems on the rise, facial expression analysis plays an important role in mental health auxiliary diagnosis and treatment (MHADT). This study proposes a facial expression localization and recognition model that integrates Main Directional Mean Optical Flow (MDMO) with the Transformer architecture. It addresses the problems of insufficient generalization ability and limited temporal modeling ability of traditional methods in facial expression recognition. The study is based on the authoritative Audio/Visual Emotion Challenge 2019 Depression Detection Sub-challenge (AVEC2019 DDS) dataset in the mental health field, which contains 163 training samples, 56 validation samples, and 56 test samples. With Explicit Shape Regression, Local Binary Patterns, Mnemonic Descent Method, Convolutional Neural Network, and benchmark models as comparison objects, it systematically evaluates the model's performance in error, accuracy, and processing speed. The results show that the model achieves the best performance in both facial expression localization and recognition tasks. The validation set errors are 7.26 and 6.85, the localization accuracy reaches 89.6%, and the recognition accuracy reaches 88.9%, which is significantly better than other methods. At the same time, its image processing time is 55 milliseconds (ms) and 44ms, balancing high precision and real-time performance. The study indicates that the fusion of MDMO and Transformer can effectively capture the spatial and temporal features of facial expressions. Thus, this fusion method provides an efficient, stable, and scalable technical solution for emotion recognition in MHADT. This improves both the FER and localization accuracy and effect. Besides, it provides a novel reference in both approaches and application level for the intelligent development of mental health evaluation.

Facial expressions are an ideal means of communicating one’s emotions or intentions to others. This overview will focus on human facial expression recognition as well as robotic facial expression generation. In the case of human facial expression recognition, both facial expression recognition on predefined datasets as well as in real-time will be covered. For robotic facial expression generation, hand-coded and automated methods i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand-coding or automatically using machine learning techniques, will also be covered. There are already plenty of studies that achieve high accuracy for emotion expression recognition on predefined datasets, but the accuracy for facial expression recognition in real-time is comparatively lower. In the case of expression generation in robots, while most of the robots are capable of making basic facial expressions, there are not many studies that enable robots to do so automatically. In this overview, state-of-the-art research in facial emotion expressions during human–robot interaction has been discussed leading to several possible directions for future research.

In order to solve the problems of low average gradient and long recognition time in traditional facial expression recognition method, a multi-scale detail enhancement method for facial dynamic expression recognition of sprint athletes is proposed. A principal component analysis method was used to establish the facial expression feature subspace of sprinters, to project and reduce the dimension of the facial dynamic expression feature vector of sprinters, and to obtain the low frequency information and high frequency information of the facial image of sprinters by bilateral filtering. The multi-scale details of expression are enhanced by using side suppression network model and improving image S curve. The feature vector of facial dynamic expression is input into support vector machine to recognise the facial dynamic expression of sprinter. Experimental results show that the average value of annoying gradient is about 98 and the shortest time s. is about 1.9.

Most automatic facial expression analysis (AFEA) systems attempt to recognize facial expressions from data collected in a highly controlled environment with very high resolution frontal faces ( face regions greater than 200 x 200 pixels). However, in real environments, the face image is often in lower resolution and with head motion. It is unclear that the performance of AFEA systems for low resolution face images. The general approach to AFEA consists of 3 steps: face acquisition, facial feature extraction, and facial expression recognition. This paper explores the effects of different image resolutions for each step of facial expression analysis. The different approaches are compared for face detection, face data extraction and expression recognition. A total of five different resolutions of the head region are studied (288x384, 144x192, 72x96, 36x48, and 18Xx24) based on a widely used public database. The lower resolution images are down-sampled from the originals.

In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection.

Facial expression recognition (FER) has been widely studied since it can be used for various applications. However, most of FER techniques focus on discriminating typical facial expressions such as six basic facial expressions. Spontaneous facial expressions are not limited to such typical ones because the intensity of a facial expression varies depending on the intensity of an emotion. In order to utilize FER for real-world applications, therefore, it is necessary to discriminate slight difference of facial expressions. In this paper, we propose an effective FER method to recognize spontaneous facial expressions using ensemble learning which combines a number of naive Bayes classifiers. In addition, a method to estimate the intensity of facial expression is also proposed by using the classification results of the classifiers. The effectiveness of these methods are evaluated through an FER experiment and an experiment to estimate the intensity of facial expressions using a data set including spontaneous facial expressions.

BackgroundPrevious reports have suggested impairment in facial expression recognition in delinquents, but controversy remains with respect to how such recognition is impaired. To address this issue, we investigated facial expression recognition in delinquents in detail.MethodsWe tested 24 male adolescent/young adult delinquents incarcerated in correctional facilities. We compared their performances with those of 24 age- and gender-matched control participants. Using standard photographs of facial expressions illustrating six basic emotions, participants matched each emotional facial expression with an appropriate verbal label.ResultsDelinquents were less accurate in the recognition of facial expressions that conveyed disgust than were control participants. The delinquents misrecognized the facial expressions of disgust as anger more frequently than did controls.ConclusionThese results suggest that one of the underpinnings of delinquency might be impaired recognition of emotional facial expressions, with a specific bias toward interpreting disgusted expressions as hostile angry expressions.

This paper proposes a unified facial motion tracking and expression recognition framework for monocular video. For retrieving facial motion, an online weight adaptive statistical appearance method is embedded into the particle filtering strategy by using a deformable facial mesh model served as an intermediate to bring input images into correspondence by means of registration and deformation. For recognizing facial expression, facial animation and facial expression are estimated sequentially for fast and efficient applications, in which facial expression is recognized by static anatomical facial expression knowledge. In addition, facial animation and facial expression are simultaneously estimated for robust and precise applications, in which facial expression is recognized by fusing static and dynamic facial expression knowledge. Experiments demonstrate the high tracking robustness and accuracy as well as the high facial expression recognition score of the proposed framework.

The Chinese Facial Emotion Recognition Database (CFERD): A computer-generated 3-D paradigm to measure the recognition of facial emotional expressions at different intensities

Four experiments are reported investigating recognition of emotional expressions in very briefly presented facial stimulus. The faces were backwardly masked by neutral facial displays and recognition of facial expressions was analyzed as a function of the manipulation of different parameters in the masking procedure. The main conclusion was that stimulus onset asynchrony between target and mask proved to be the principal factor influencing recognition of the masked expressions. In general, confident recognitions of facial expressions required about 100-150 msec, with shorter time for happy than for angry expressions. The manipulation of the duration of both the target and the mask, by itself, had only minimal effects.

Facial expression recognition (FER) techniques can be widely used in human-computer interaction, intelligent robots, intelligent monitoring, and other domains. Currently, FER methods based on deep learning have become the mainstream schemes. However, these methods have some problems, such as a large number of parameters, difficulty in being applied to embedded processors, and the fact that recognition accuracy is affected by facial deflection. To solve the problem of a large number of parameters, we propose a DSC-DenseNet model, which improves the standard convolution in DenseNet to depthwise separable convolution (DSC). To solve the problem wherein face deflection affects the recognition effect, we propose a posture normalization model based on GAN: a GAN with two local discriminators (LD-GAN) that strengthen the discriminatory abilities of the expression-related local parts, such as the parts related to the eyes, eyebrows, mouth, and nose. These discriminators improve the model’s ability to retain facial expressions and evidently benefits FER. Quantitative and qualitative experimental results on the Fer2013 and KDEF datasets have consistently shown the superiority of our FER method when working with multi-pose face images.