Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

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

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.

Sparse representation in compressed sensing is a hot topic in signal processing and artificial intelligence due to its success in various applications. A general classification algorithm based on sparse representation theory named Sparse Representation Classification (SRC) was successfully applied in face recognition. In this paper, we research the issue of facial expression recognition (FER) via SRC. Extensive experiments of FER via SRC algorithm are carried out to study the performance of sparse representation theory for FER. The comparison of SRC algorithm with various traditional algorithms such as two-dimensional PCA as well as curvelet transform for FER is also given. Support vector machine is used for expression classification. The paper also studies the robustness of SRC algorithm for FER to noises. Satisfactory results are obtained for FER via sparse representation. The experiment results show the effectiveness of SRC algorithm on FER.

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/Objectives: There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images. Methods: We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution images for facial expression recognition (Anger, Sad, Disgust, Happy, Surprise, Neutral, Fear), so we created a Low Resolution Facial Expression (LRFE) dataset, which contains more than 6000 images of seven types of facial expressions. The existing FER2013 dataset and LRFE dataset were used. These datasets were divided in the ratio 80:20 for training and testing and validation purpose. A HDM is proposed, which is a combination of Gaussian Filter, Bilateral Filter and Non local means denoising Filter. This hybrid denoising method helps us to increase the performance of the convolutional neural network. The proposed model was then compared with VGG16 and VGG19 models. Findings: The experimental results show that the proposed FERConvNet_HDM approach is effective than VGG16 and VGG19 in facial expression recognition on both FER2013 and LRFE dataset. The proposed FERConvNet_HDM approach achieved 85% accuracy on Fer2013 dataset, outperforming the VGG16 and VGG19 models, whose accuracies are 60% and 53% on Fer2013 dataset respectively. The same FERConvNet_HDM approach when applied on LRFE dataset achieved 95% accuracy. After analyzing the results, our FERConvNet_HDM approach performs better than VGG16 and VGG19 on both Fer2013 and LRFE dataset. Novelty/Applications: HDM with convolutional neural networks, helps in increasing the performance of convolutional neural networks in Facial expression recognition. Keywords: Facial expression recognition; facial emotion; convolutional neural network; deep learning; computer vision

Face recognition has many important applications in areas such as public surveillance and security, identity verification in the digital world, and modeling techniques in multimedia data management. Facial expression recognition is also important for targeted marketing, medical analysis, and human–robot interaction. In this paper, we survey a few techniques for facial analysis. We compare the cloud platform AWS Rekognition, convolutional neural networks, transfer learning from pre-trained neural nets, and traditional feature extraction using facial landmarks for this analysis. Although not comprehensive, this survey covers a lot of ground in the state-of-the-art solutions for facial analysis. We show that to get high accuracy, good-quality data and processing power must be provided in large quantities. We present the results of our experiments which have been conducted over six different public as well as proprietary image data sets.

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.

Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l¹ -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.

Cognitive domains related to attention and executive functions (a set of cognitive processes that regulate, control, and manage other cognitive abilities) seem to influence the recognition of facial expressions in people with Alzheimer's disease (AD). We examined the relationship between facial expression recognition, global cognition and executive function in people with AD according to their cognitive level. In a cross-sectional design, we included 130 participants with AD divided into three groups based on their Mini-Mental State Examination (MMSE) scores: MMSE 1 (scores 23-28), MMSE 2 (scores 17-22), and MMSE 3 (scores 11-16). Facial expression recognition ability was analyzed using the Faces Test. Executive function was analyzed using the Trail Making Test (TMT), the Verbal Fluency Test (VFT), the Semantic Fluency Test (SFT), the Digit Span Forward (DSF) and Backward (DSB) tests, and the Clock Drawing Test (CDT). In MMSE 1 group difficulties in divided attention and cognitive flexibility impacted the accuracy of facial expression recognition. In the MMSE 2 group, facial expression recognition was related to impairment in working memory. In the MMSE 3 group, the impact on facial expression recognition was directly related to visuoconstructive abilities. We observed that the executive resources involved in each evaluated group differed in terms of facial recognition task performance efficacy. Interventions at stimulating executive and visuoconstructive abilities in people with AD may contribute to better preservation of facial expression recognition.

The ability to recognize facial expressions of emotion is vita l for effective social interaction. Facial expressions convey emotional cues (Adolphs, 2002, 2003; Darwin, 1872/1965; Ekman, 1992, 1993) and accurate recognition of these cues is a necessary step in the evaluation of interpersonal interactions and for the subsequent application of appropriate social skills (Collins & Nowicki, 2001; Hampson, van Anders, & Mullin, 2006; Philippot & Feldman, 1990). Research has shown support for the association between facial expression recognition (FER) abilities and social competency (Custrini & Feldman, 1989; Lancelot & Nowicki, 1997; Nowicki & Duke, 1994); relationship difficulties (Barth & Bastiani, 1997; Blair & Coles, 2000; Lancelot & Nowicki, 1997); and various psychological and psychiatric conditions (Blair et al., 2004; Penn & Combs, 2000; Russell, Chu & Phillips, 2006; Silver, Goodman, Knoll, & Isakov, 2004; van Beek & Dubas, 2008), including anxiety (Easter et al., 2005), bipolar disorder (Brotman et al., 2008), and psychopathology (Blair et al., 2004). Other studies have shown support for associations between FER deficits and childhood maltreatment (Pears & Fisher, 2005; Pollak, Cicchetti, Hornung, & Reed, 2000) and attachment (Cooley, 2005; Magai, Distel & Liker, 1995; Niedenthal, Brauer, Robin & Innes-Ker, 2002). According to emotions theorist Izard (2002), the inability to recognize nonverbal forms of emotion expression can negatively affect intra-and interpersonal behavior, and may serve as a risk factor for poor adjustment and future adverse outcomes. Although the particular mechanisms underlying FER are under debate, the study of the ability to recognize facial expressions of emotion continues to be the subject of research within various fields, including psychology (Hall, 2006; Ekman, Friesen, & Tomkins, 1971; Nowicki & Carton, 1992), psychiatry (Herba & Phillips, 2004; Wang, Lee, Sigman, & Dapretto, 2007), as well as computer science and philosophy (Adolphs, 2002). Facial expression recognition research findings have been provided from varied perspectives, such as neuroscience (Wang et al., 2007); social-cognitive processing and early childhood experiences (Masten et al., 2008; Mullins & Duke, 2004; & Pollack & Sinha, 2000); as well as perception (Pollak, Messner, Kisler, & Cohn, 2009). Other researchers have integrated information from differing fields, such as social psychology and perception (Calder, Young, Keane, & Dean, 2000; Pollak et al., 2009) and behavior studies and neuroscience (Herba & Phillips, 2004). Thus, facial recognition research appears to be bridging some of the natural gaps caused by distinct fields of investigation. The authors of this paper will attempt to present findings from FER studies that are particularly relevant for behavioral psychologists. Following a brief history of the study of FER, we highlight findings from various fields (e.g., social psychology, nonverbal processing), that focused on FER with particular clinical populations. Discussion of suggested clinical implications and applications from a clinical behavioral perspective, and suggestions for future research directions, concludes this article on the importance of FER in clinical work. History of Facial Expression Recognition Study Current research and public interest in facial expression recognition stems from a rich history. Scientific study and understanding of emotion is thought to have begun in the 19th century with Charles Darwin's The Expression of the Emotions in Man and Animals (originally published in 1872) and G.G. Duchenne de Bologne's The Mechanism of Human Facial Expression (originally published in 1862) (Mayne & Bonanno, 2001). These early works focused on the important role of facial displays in emotional life and introduced the theory that emotions may be understood as biologically-based reflex behaviors serving adaptive functions. …

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.

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

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

These Human facial expressions convey a lot of information visually rather than articulately. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications including, but not limited to, human behaviour understanding, detection of mental disorders, and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Two popular methods utilized mostly in the literature for the automatic FER systems are based on geometry and appearance. Facial Expression Recognition usually performed in four-stages consisting of pre-processing, face detection, feature extraction, and expression classification. The human face is an important part of an individual’s body and plays an important role in knowing the individual’s mood. The face is where a human expresses all his basic emotions. In the existing system, they examine the mental state manually by assessing them but have many disadvantages like we cannot predict any accurate solutions based on the assessment score because we might be not sure what kind of emotions the human user would be all time. To overcome this problem, a novel system is proposed to suggest an effective solution for predicting the mental state dynamically, we propose a hybrid architecture invoking facial based emotion sequence, PEN test and IQ test. By consistent monitoring of a human’s emotion and subjecting to PEN and IQ tests, the human’s mental state is routed. Combination of the above three techniques provides promising results for mental state and self-control

Facial recognition techniques, fantasized in fiction movie classics, have already become reality. Such technology opens up a wide range of possibilities for different kinds of systems. From the point of view of interactive applications, facial expression as input data may be more immediate and more trustworthy to the user’s sentiment than the click of a button. For interactive television, facial expression recognition could be used for bringing broadcasters and viewers closer, enabling TV content to be personalized by the user sentiment. In fact, not only facial expression recognition, but any interaction that enables affective computing. In this work, we call this concept Affective TV. In order to support it, this work proposes facial expression recognition for digital TV applications. Our proposal is implemented and evaluated in the Ginga-NCL middleware, a digital TV standard used in several Latin American countries.