A Three-Dimensional Anisotropic Diffusion Equation-Based Video Recognition Model for Classroom Concentration Evaluation in English Language Teaching

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.

Several researchers have attempted automatic recognition and tracking of facial expressions in video frames of neutral and expressive faces. But, it is very hard to detect a neutral face in practical applications because the distinction between facial movement and head movement is difficult to make. To solve this distinction problem, we tried to initialize the head position using the central point of each eye and the tip of the nose to extract facial movements. Based on the detected neutral faces, the changing processes of facial expressions can be recognized by means of detecting the reference face (neutral face) and an algorithm of the recognition for facial expressions. In short, we assume that this method of detecting neutral faces can be applied to the recognition of facial expressions.

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.

Texture characteristics among the landmark points reflected in human faces are important features in the recognition of facial expression. In this paper, we propose a salient feature based facial expression on texture characteristics. Detection of effective landmarks are performed by appearance based models and corresponding texture regions are extracted from face images. Local Binary Pattern (LBP) is used to compute the texture feature. Texture feature is used for normalizing texture signatures. In addition, we introduce stability indices corresponding to texture features which are exploited as features in the present scope. Moreover, statistical features such as moment, skewness, kurtosis and entropy are also supplemented to the feature set. These salient features together are used as an input to Nonlinear AutoRegressive with eXogenous (NARX) for recognition of the human facial expression. The Cohn-Kanade (CK+), Japanese Female Facial Expression (JAFFE), MMI and MUG benchmark databases are used to conduct the experiments and the results justify the effectiveness of the proposed procedure.

For the wavelet transform has limitations in extract features of the edge of images, a method of the facial expression recognition is proposed that using curvelet transform to extract features of the edge of images. The curvelet transform can get more representation of sparse images than the wavelet transform on the representation of the singular of the edges of image curve. The curvelet coefficient that can be got by using the curvelet transform on the facial images as the edge of the texture features can better reflect the change in facial expression. As the same time, the k-nearest neighbor algorithm is used to recognize different expression in this paper. The result shows that the proposed algorithm in this paper is more effective than the wavelet transform in expression recognition.

In this paper, a person-specific facial expression recognition method which is based on Personal Facial Expression Space (PFES) is presented. The multidimensional scaling maps facial images as points in lower dimensions in PFES. It reflects personality of facial expressions as it is based on the peak instant of facial expression images of a specific person. In constructing PFES for a person, his/her whole normalized facial image is considered as a single pattern without block segmentation and differences of 2-D DCT coefficients from neutral facial image of the same person are used as features. Therefore, in the early part of the paper, separation characteristics of facial expressions in the frequency domain are analyzed using a still facial image database which consists of neutral, smile, anger, surprise and sadness facial images for each of 60 Japanese males (300 facial images). Results show that facial expression categories are well separated in the low frequency domain. PFES is constructed using multidimensional scaling by taking these low frequency domain of differences of 2-D DCT coefficients as features. On the PFES, trajectory of a facial image sequence of a person can be calculated in real time. Based on this trajectory, facial expressions can be recognized. Experimental results show the effectiveness of this method.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model ) and non-pre-trained model convolutional neural networks (CNNs) for the task of image classification. The VGG19 network and convolutional network derive two channels of expression related characteristics from the facial grayscale images. The linear fusion algorithm calculates the class by taking an average of each classification decision on training samples of both channels. Final recognition is calculated using convolution neural network architecture followed by a softmax classifier. Seven basic facial emotions (BEs): happiness, surprise, anger, sadness, fear, disgust, and neutral facial expressions can be recognized by the proposed algorithm, The average accuracies for standard data set’s “CK+,” and “JAFFE,” 98.3 % and 92.4%, respectively. Using a deep network with one channel, the proposed algorithm can achieve well comparable performance.

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.

Deals with a neural network method for the machine recognition of mixed facial expressions by decomposing mixed facial expression into 2 or 3 components of 6 basic ones. The authors obtain the facial images, which show mixed facial expressions, from video tape recorded facial images and from the information of facial expressions in terms of the (x,y) coordinates of facial characteristic points. Then the position information of facial image is generated for 19 clients, and is used for the neural network training and recognition test. The recognition test is done by inputting the facial information, not being used in training the neural network, to the trained neural network. The recognition results obtained by the neural network are compared with those by humans. The neural network method is found to give a rather high agreement rate of about 70% compared with those obtained by humans. >

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.

Facial expression recognition across domains, e.g., training and testing facial images come from different facial poses, is very challenging due to the different marginal distributions between training and testing facial feature vectors. To deal with such challenging cross-domain facial expression recognition problem, a novel transductive transfer subspace learning method is proposed in this paper. In this method, a labelled facial image set from source domain is combined with an unlabelled auxiliary facial image set from target domain to jointly learn a discriminative subspace and make the class labels prediction of the unlabelled facial images, where a transductive transfer regularized least-squares regression (TTRLSR) model is proposed to this end. Then, based on the auxiliary facial image set, we train a SVM classifier for classifying the expressions of other facial images in the target domain. Moreover, we also investigate the use of color facial features to evaluate the recognition performance of the proposed facial expression recognition method, where color scale invariant feature transform (CSIFT) features associated with 49 landmark facial points are extracted to describe each color facial image. Finally, extensive experiments on BU-3DFE and Multi-PIE multiview color facial expression databases are conducted to evaluate the cross-database & cross-view facial expression recognition performance of the proposed method. Comparisons with state-of-the-art domain adaption methods are also included in the experiments. The experimental results demonstrate that the proposed method achieves much better recognition performance compared with the state-of-the-art methods.

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated with facial expressions is represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold to compute a posterior probability associated with a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89% recognition rate in a set of 333 sequences from the Cohn–Kanade database.

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.

In order to improve the recognition of facial expression recognition rate, facial expression recognition algorithm is based on a LBP and SVM decision tree. First will facial expression image is converted to LBP characteristic spectrum using LBP algorithm, and then the LBP characteristic spectrum into LBP histogram feature sequence, finally completes the classification and recognition of facial expression by SVM decision tree algorithm, and prove the effectiveness of the proposed method in the recognition of facial expression database in JAFFE.

Many Facial expression recognition methods have achieved great success, but they only considered front facial images or facial images close to the front. Besides, unlike in-the-laboratory datasets, the facial images in the real world (or in the wild) are without lighting and pose control, so that it is a big challenge to recognize these facial expressions. In this paper, the PIDViT (i.e., Pose-Invariant Distilled Vision Transformer) using the teacher-student model for the probability distributions of facial expressions of frontal and multi-pose faces was proposed and solved the pose variance and occlusion issues on expression recognition. First, the multi-pose face dataset FairFace-3D was built from the original FairFace and then used to train pose-invariance on the PIDViT. The PIDViT was trained in two stages; stage 1 is to train the PIDViT to achieve the consistency of facial expressions between frontal faces and multi-pose faces, and stage 2 is to use the student model pre-trained in stage 1 and train facial expressions further on target datasets. Finally, comprehensive experiments were conducted on three in the wild facial expression datasets, and the results validates the generalization of the PIDViT and its superiority over most state-of-the-art models.