Facial expression recognition based on LBP and SVM decision tree

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.

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.

The recognition rate of person-independent facial expression is generally not high, which limits the practical application of facial expression recognition. Aiming at this problem, this paper analyzes the reasons for the low recognition rate of person-independent facial expression, and proposes a recognition algorithm of person-independent facial expression based on improved LBP (Local Binary Pattern) and HOSVD (Higher-Order Singular Value Decomposition). The algorithm has the following contributions of facial expression recognition framework. In the stage of facial expression feature extraction, the transient features extracted by LDP(Local Directional Pattern) and the persistent features extracted by CBP(Centralized Binary Pattern) are integrated to improve the discrimination of facial expression features. Moreover, in the stage of facial expression classification and recognition, the traditional nearest neighbor classification is changed into k-nearest neighbor pre-classification, and the regional energy calculated by HOSVD is used to determine the similarity of two images for secondary classification. Finally, in the extended Cohn-Kanade dataset and Oulu-CASIA NIR&VIS facial expression database, the theoretical analysis and experimental results show that the method has better recognition effect for solving the problem of person-independent facial expression recognition.

In this paper, we proposed a novel algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Local Phase Quantization (LPQ). Firstly, the LPQ feature and gabor texture feature were respectively extracted from every expression image. LPQ features are histograms of LPQ transform. Five scales and eight orientations of gabor wavelet filters are used to extract gabor texture features and adaboost algorithm is used to select gabor features. Then we obtain two expression recognition results on both expression features by Sparse Representation-based Classification (SRC) method. Finally, the final expression recognition was performed by fusion of residuals of two SRC algorithms. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm was better than the original two algorithms, and this algorithm had a much higher recognition rate than the traditional algorithm.

In this paper, we propose a novel approach for facial expression decomposition - higher-order singular value decomposition (HOSVD), a natural generalization of matrix SVD. We learn the expression subspace and person subspace from a corpus of images showing seven basic facial expressions, rather than resort to expert-coded facial expression parameters. We propose a simultaneous face and facial expression recognition algorithm, which can classify the given image into one of the seven basic facial expression categories, and then other facial expressions of the new person can be synthesized using the learned expression subspace model. The contributions of this work lie mainly in two aspects. First, we propose a new HOSVD based approach to model the mapping between persons and expressions, used for facial expression synthesis for a new person. Second, we realize simultaneous face and facial expression recognition as a result of facial expression decomposition. Experimental results are presented that illustrate the capability of the person subspace and expression subspace in both synthesis and recognition tasks. As a quantitative measure of the quality of synthesis, we propose using gradient minimum square error (GMSE) which measures the gradient difference between the original and synthesized images.

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.

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 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.

With the rise of artificial intelligence, machine learning (ML) is increasingly integrated into daily life. Facial expressions, lasting about 1/20th of a second and difficult to conceal, convey nuanced emotions beyond words. They are categorized as macro expressions, displayed under normal circumstances, and micro expressions, fleeting and subconscious. However, recognizing expressions in photos is challenging due to varied backgrounds, appearances, age, and race, impacting accuracy. Addressing this, our research focuses on a machine learning-based facial expression recognition algorithm. Our experimental results, using CASIA and NTERFACE databases, demonstrate that ML outperforms KNN and ANN with recognition rates 41.54% and 26.94% higher, respectively. By adaptively learning global and local features from complete expression images and face parts, our algorithm offers practical and significant advancements in expression recognition.

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.

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.

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.

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.

It is fascinating to hear about the research conducted on facial expression recognition algorithms and its significance in the era of intelligence, especially with the development of the internet and IoT. The use of deep learning method and neural network VGG to study facial expressions sounds promising. It is encouraging to know that the network structure was improved and pooled using Soft pool, thereby enhancing the extraction of facial expression features. The integration of an attention mechanism to improve the ability of the network to recognize facial expressions appears to be a valuable addition to the research. it is impressive that the recognition rate of the improved network on the face expression dataset RAF-DB reached 86.1%, exceeding that of the original VGG network on this data set. this outcome suggests that the changed network is effective for facial expression recognition. thank you for sharing this information.

This paper proposes a method of facial expression recognition based on Zernike moments and the minimum classification error (MCE) based hidden Markov model (HMM). In the feature extraction of face, the method of Zernike moments feature extraction based on local feature regions is adopted. First, eyes and mouths are segmented from the facial expression image and Zernike moments feature vectors of eyes and mouth are extracted. In the classification of expression, using MCE criteria is adopted to improve the performance of HMM. The results show that the performance of expression recognition system can be improved to some extent by using Zernike moments and MCE criterion.