Facial expression recognition using clustering discriminant Non-negative Matrix Factorization

Non-negative Matrix Factorization (NMF) is among the most popular subspace methods widely used in a variety of pattern recognition applications. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis criteria and achieves an efficient decomposition of the provided data to its salient parts has been proposed. An extension of this work specialized for classification, optimized using projected gradients in order to ensure converge to a stationary limit point, resulted in a more efficient method of the latter approach. Assuming multimodality of the underlying data samples distribution and incorporating clustering discriminant inspired constraints into the NMF decomposition cost function, resulted in the Subclass Discriminant NMF algorithm which found to outperform both approaches under real life settings. In this work we review all these methods in the context of various pattern recognition problems using facial images.

Face and facial expression recognition is a broad research domain in machine learning domain. Non-negative matrix factorization (NMF) is a very recent technique for data decomposition and image analysis. Here we propose face identification system as well as a facial expression recognition, which is a system based on NMF. We get a significant result for face recognition. We test on CK+ and JAFFE dataset and we find the face identification accuracy is nearly 99% and 96.5% respectively. But the facial expression recognition (FER) rate is not as good as it required for the real life implementation. To increase the detection rate for facial expression recognition, our propose fusion based NMF, named as OEPA-NMF, where OEPA means Optimal Expression specific Parts Accumulation. Our experimental result shows OEPA-NMF outperforms the prevalence NMF for facial expression recognition. As face identification using NMF has a good accuracy rate, so we are not interested to apply OEPA-NMF for face identification.

BackgroundAlcohol abuse and dependence can cause a wide variety of cognitive, psychomotor, and visual-spatial deficits. It is questionable whether this condition is associated with impairments in the recognition of affective and/or emotional information. Such impairments may promote deficits in social cognition and, consequently, in the adaptation and interaction of alcohol abusers with their social environment. The aim of this systematic review was to systematize the literature on alcoholics’ recognition of basic facial expressions in terms of the following outcome variables: accuracy, emotional intensity, and latency time.MethodsA systematic literature search in the PsycINFO, PubMed, and SciELO electronic databases, with no restrictions regarding publication year, was employed as the study methodology.ResultsThe findings of some studies indicate that alcoholics have greater impairment in facial expression recognition tasks, while others could not differentiate the clinical group from controls. However, there was a trend toward greater deficits in alcoholics. Alcoholics displayed less accuracy in recognition of sadness and disgust and required greater emotional intensity to judge facial expressions corresponding to fear and anger.ConclusionThe current study was only able to identify trends in the chosen outcome variables. Future studies that aim to provide more precise evidence for the potential influence of alcohol on social cognition are needed.

Facial expression recognition is a topic of interest both in industry and academia. Recent approaches to facial expression recognition are based on mapping expressions to low dimensional manifolds. In this paper we revisit various dimensionality reduction algorithms using a graph-based paradigm. We compare eight dimensionality reduction algorithms on a facial expression recognition task. For this task, experimental results show that although Linear Discriminant Analysis (LDA) is the simplest and oldest supervised approach, its results are comparable to more flexible recent algorithms. LDA, on the other hand, is much simpler to tune, since it only depends on one parameter.

A novel approach to facial expression recognition (FER) based on the combination of Non-negative Matrix Factorization (NMF) and Support Vector Machine (SVM) was proposed. One key step in FER is to extract expression features from the original face images. NMF is an effective approach to extract expression features because NMF decomposition makes the reconstruction of expression images in a non-subtractive way and is much similar to the process of forming unity from parts. The proposed algorithm first processes facial expression image with histogram equalization operator. Then NMF method is used for feature dimension reduction and SVM for classification. Finally, the algorithm was implemented with Matlab and experimented in Japanese female facial expression database (JAFEE database). A recognition rate of 66.19% was obtained and showed the effectiveness of the proposed algorithm

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.

Traditional Nonnegative Matrix Factorization (NMF) is a linear and unsupervised algorithm. This would limit the classification power of NMF for the complicated data. To overcome the above limitations of NMF, this paper proposes a novel supervised and nonlinear NMF algorithm based on kernel theory and discriminant analysis. We incorporate the class label information into the decomposition of NMF in the Reproducing Kernel Hilbert Space (RKHS). A new iterative algorithm for NMF is derived and the objective function is non-increasing under the update rules. The proposed method is evaluated on the ORL and Yale face databases. The experimental results demonstrate the the proposed method is superior to the state of-the-art algorithms.

Facial expression recognition (FER) is utilized in various fields that analyze facial expressions. FER is attracting increasing attention for its role in improving the convenience in human life. It is widely applied in human–computer interaction tasks. However, recently, FER tasks have encountered certain data and training issues. To address these issues in FER, few-shot learning (FSL) has been researched as a new approach. In this paper, we focus on analyzing FER techniques based on FSL and consider the computational complexity and processing time in these models. FSL has been researched as it can solve the problems of training with few datasets and generalizing in a wild-environmental condition. Based on our analysis, we describe certain existing challenges in the use of FSL in FER systems and suggest research directions to resolve these issues. FER using FSL can be time efficient and reduce the complexity in many other real-time processing tasks and is an important area for further research.

Recent deep neural networks based methods have achieved state-of-the-art performance on various facial expression recognition tasks. Despite such progress, previous researches for facial expression recognition have mainly focused on analyzing color recording videos only. However, the complex emotions expressed by people with different skin colors under different lighting conditions through dynamic facial expressions can be fully understandable by integrating information from multi-modal videos. We present a novel method to estimate dimensional emotion states, where color, depth, and thermal recording videos are used as a multi-modal input. Our networks, called multi-modal recurrent attention networks (MRAN), learn spatiotemporal attention volumes to robustly recognize the facial expression based on attention-boosted feature volumes. We leverage the depth and thermal sequences as guidance priors for color sequence to selectively focus on emotional discriminative regions. We also introduce a novel benchmark for multi-modal facial expression recognition, termed as multi-modal arousal-valence facial expression recognition (MAVFER), which consists of color, depth, and thermal recording videos with corresponding continuous arousal-valence scores. The experimental results show that our method can achieve the state-of-the-art results in dimensional facial expression recognition on color recording datasets including RECOLA, SEWA and AFEW, and a multi-modal recording dataset including MAVFER.

The objective of single-channel source separation is to accurately recover source signals from mixtures. Non-negative matrix factorization (NMF) is a popular approach for this task, yet previous NMF approaches have not optimized directly this objective, despite some efforts in this direction. Our paper introduces discriminative training of the NMF basis functions such that, given the coefficients obtained on a mixture, a desired source is optimally recovered. We approach this optimization by generalizing the model to have separate analysis and reconstruction basis functions. This generalization frees us to optimize reconstruction objectives that incorporate the filtering step and SNR performance criteria. A novel multiplicative update algorithm is presented for the optimization of the reconstruction basis functions according to the proposed discriminative objective functions. Results on the 2nd CHiME Speech Separation and Recognition Challenge task indicate significant gains in source-to-distortion ratio with respect to sparse NMF, exemplar-based NMF, as well as a previously proposed discriminative NMF criterion.

<p>This thesis addresses three key challenges in gaze estimation and facial expression recognition datasets. Firstly, we tackle the issue of annotation inconsistency in gaze datasets by introducing the Gaze Adaptor Module (GAM) to ensure consistent gaze labels across different datasets. Additionally, we propose the Two-stage Transformer-based Gaze-feature Fusion (TTGF) architecture to enhance the fusion of face and eye features, resulting in improved gaze estimation accuracy. Secondly, we address the problem of imbalanced distribution in facial expression recognition datasets. To mitigate bias during training, we propose a recall-based weighting (RBW) strategy, which adjusts the weighting for expressions and demographic categories (such as gender) using the weighted cross-entropy loss. We also introduce a code-mixing synthesis approach to generate a balanced-synthesized dataset, and companion training with the synthesized dataset (CTSD) to reduce model bias. These techniques significantly improve the fairness and accuracy of facial expression recognition models. Lastly, we tackle the challenge of emotional frame sparsity in video datasets. We propose a novel model that incorporates an extra regression token to integrate features from all frames within each video. This enables effective handling of videos with varying lengths and addresses the issue of sparse emotional reaction frames. Experimental results demonstrate the superiority of our architecture in accurately estimating emotional reaction intensity in videos. By addressing these challenges, this thesis contributes to the advancement of gaze estimation and facial expression recognition.</p>

Current discriminant nonnegative matrix factorization (NMF) methods either do not guarantee convergence to a stationary limit point or assume a compact data distribution inside classes, thus ignoring intra class variance in extracting discriminant data samples representations. To address both limitations, we regard that data inside each class has a multimodal distribution, forming various subclasses and perform optimization using a projected gradients framework to ensure limit point stationarity. The proposed method combines appropriate clustering-based discriminant criteria in the NMF decomposition cost function, in order to find discriminant projections that enhance class separability in the reduced dimensional projection space, thus improving classification performance. The developed algorithms have been applied to facial expression, face and object recognition, and experimental results verified that they successfully identified discriminant parts, thus enhancing recognition performance.

Facial Expression Recognition (FER) seeks to classify affective states from facial images, which remains a challenging problem due to variations in real-world conditions. FER task becomes particularly complex when handling unconstrained environments characterized by partial occlusions, different head poses, and so on. To address the above problems, current approaches rely on extensive learnable parameters and complex model architectures, which inevitably lead to overfitting and cause the FER model to focus on non-discriminative facial regions. In this work, we propose an HKAFER model that can adaptively enhance visual expression representations through efficiently fine-tuning the image encoder in large Visual Foundation Models (VFMs) and Vision-Language Models (VLMs). Specifically, we establish Heterogeneous Kronecker Adaptation (HeKA), which consists of multi-scale adapters based on Kronecker product in a parallel manner, offering significantly diverse subspaces to learn the incremental matrices. Besides, we also propose Dual-Branch Interactive Router (DBIR) to dynamically assign the weights of adapters, which promotes collaboration and information flow among them. In this way, our HKAFER can effectively capture robust spatial features and the regional associations. Experimental results demonstrate that our proposed model not only outperforms state-of-the-art methods on several FER benchmarks but also uses significantly fewer trainable parameters.

Facial expression recognition (FER) in the wild is a challenging task due to some uncontrolled factors such as occlusion, illumination, and pose variation. The current methods perform well in controlled conditions. However, there are still two issues with the in-the-wild FER task: (i) insufficient descriptions of long-range dependency of expression features in the facial information space and (ii) not finely refining subtle inter-classes distinction from multiple expressions in the wild. To overcome the above issues, an end-to-end model for FER, named attention-modulated contextual spatial information network (ACSI-Net), is presented in this paper, with the manner of embedding coordinate attention (CA) modules into a contextual convolutional residual network (CoResNet). Firstly, CoResNet is constituted by arranging contextual convolution (CoConv) blocks of different levels to integrate facial expression features with long-range dependency, which generates a holistic representation of spatial information on facial expression. Then, the CA modules are inserted into different stages of CoResNet, at each of which the subtle information about facial expression acquired from CoConv blocks is first modulated by the corresponding CA module across channels and spatial locations and then flows into the next layer. Finally, to highlight facial regions related to expression, a CA module located at the end of the whole network, which produces attentional masks to multiply by input feature maps, is utilized to focus on salient regions. Different from other models, the ACSI-Net is capable of exploring intrinsic dependencies between features and yielding a discriminative representation for facial expression classification. Extensive experimental results on AffectNet and RAF_DB datasets demonstrate its effectiveness and competitiveness compared to other FER methods.

Sparse coding is currently an active topic in signal processing and pattern recognition. Meta Face Learning (MFL) isatypical sparse coding method and exhibits promising performance for classification. Unfortunately, due to using the l1-norm minimization, MFLis expensive to compute and is not robust enough. To address these issues, this paper proposesafaster and more robust version of MFLwith the l2-norm regularization constraint on coding coefficients. The proposed method is used to learnaclass-specific dictionary for facial expression recognition. Extensive experiments on two popular facial expression databases, i.e., the JAFFEdatabase and the Cohn-Kanade database, demonstrate that our method shows promising computational efficiency and robustness on facial expression recognition tasks.