Cross-Domain Color Facial Expression Recognition Using Transductive Transfer Subspace Learning

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.

A novel 2D and 3D multimodal approach for in-the-wild facial expression recognition

Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

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.

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.

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.

Facial expression recognition based on Local Binary Patterns: A comprehensive study

Recently, facial expression recognition has attracted much attention in machine vision research because of its various applications. Accordingly, many facial expression recognition systems have been proposed. However, the majority of existing systems suffer from a critical problem: geometric variability. It directly affects the performance of geometric feature-based facial expression recognition approaches. Furthermore, it is a crucial challenge in appearance feature-based techniques. This variability appears in both neutral faces and facial expressions. Appropriate face geometry normalization can improve the accuracy of each facial expression recognition system. Therefore, this paper proposes different geometric models or shapes for normalization. Face geometry normalization removes geometric variability of facial images and consequently, appearance feature extraction methods can be accurately utilized to represent facial images. Thus, some expression-based geometric models are proposed for facial image normalization. Next, local binary patterns and local phase quantization are used for appearance feature extraction. A combination of an effective geometric normalization with accurate appearance representations results in more than a 4% accuracy improvement compared to several state-of-the-arts in facial expression recognition. Moreover, utilizing the model of facial expressions which have larger mouth and eye region sizes gives higher accuracy due to the importance of these regions in facial expression.

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.

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.

Domain Adaptation (DA) methods are usually carried out by means of simply reducing the marginal distribution differences between the source and target domains, and subsequently using the resultant trained classifier, namely source classifier, for use in the target domain. However, in many cases, the true predictive distributions of the source and target domains can be vastly different especially when their class distributions are skewed, causing the issues of sample selection bias in DA. Hence, DA methods which leverage the source labeled data may suffer from poor generalization in the target domain, resulting in negative transfer. In addition, we observed that many DA methods use either a source classifier or a linear combination of source classifiers with a fixed weighting for predicting the target unlabeled data. Essentially, the labels of the target unlabeled data are spanned by the prediction of these source classifiers. Motivated by these observations, in this paper, we propose to construct many source classifiers of diverse biases and learn the weight for each source classifier by directly minimizing the structural risk defined on the target unlabeled data so as to heal the possible sample selection bias. Since the weights are learned by maximizing the margin of separation between opposite classes on the target unlabeled data, the proposed method is established here as Maximal Margin Target Label Learning (MMTLL), which is in a form of Multiple Kernel Learning problem with many label kernels. Extensive experimental studies of MMTLL against several state-of-the-art methods on the Sentiment and Newsgroups datasets with various imbalanced class settings showed that MMTLL exhibited robust accuracies on all the settings considered and was resilient to negative transfer, in contrast to other counterpart methods which suffered significantly in prediction accuracy.

In order to achieve high recognition rate, most facial expression recognition (FER) methods generate sufficient labeled facial images based on generative adversarial networks (GAN) to train model. However, these methods do not estimate the facial pose before passing the images to the generator, which affects the quality of generated images. And mode collapse is prone to occur during the training process, leading to generate a single-style facial images. To solve these problems, a FER model is proposed based on pose conditioned dendritic convolution neural network (PCD-CNN) with pose and expression. Before passing the facial images to the generator, PCD-CNN was used to process facial images, effectively estimating the facial landmarks to detect face and disentangle the pose. In order to accelerate the training speed of the model, PCD-CNN was based on the ShuffleNet-v2 framework. Every landmark of facial image was modeled by a separate ShuffleNet-DeconvNet, maintaining better performance with fewer parameters. To solve the mode collapse during image generation, we theoretically analyzed the causes, and implemented mini-batch processing on the discriminator in the model and directly calculated the statistical characteristics of the mini-batch samples. Experiments were carried out on the Multi-PIE and BU-3DFE facial expression datasets. Compared with current advanced methods, our method achieves higher accuracy 93.08%, and the training process is more stable.

ExprADA: Adversarial domain adaptation for facial expression analysis

Facial expression recognition (FER) plays an important role in human–computer interaction. The recent years have witnessed an increasing trend of various approaches for the FER, but these approaches usually do not consider the effect of individual differences to the recognition result. When the face images change from neutral to a certain expression, the changing information constituted of the structural characteristics and the texture information can provide rich important clues not seen in either face image. Therefore it is believed to be of great importance for machine vision. This study proposes a novel FER algorithm by exploiting the structural characteristics and the texture information hiding in the image space. Firstly, the feature points are marked by an active appearance model. Secondly, three facial features, which are feature point distance ratio coefficient, connection angle ratio coefficient and skin deformation energy parameter, are proposed to eliminate the differences among the individuals. Finally, a radial basis function neural network is utilised as the classifier for the FER. Extensive experimental results on the Cohn–Kanade database and the Beihang University (BHU) facial expression database show the significant advantages of the proposed method over the existing ones.