Facial expression recognition using graph-based features and artificial neural network

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.

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.

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

Peer-to-Peer (P2P) lending is an online platform to facilitate borrowing and investment transactions. A central problem for these P2P platforms is how to identify the most influential factors that are closely related to the credit risks. This problem is inherently complex due to the various forms of risks and the numerous influencing factors involved. Moreover, raw data of P2P lending are often high-dimension, highly correlated and unstable, making the problem more untractable by traditional statistical and machine learning approaches. To address these problems, we develop a novel filter-based feature selection method for P2P lending analysis. Unlike most traditional feature selection methods that use vectorial features, the proposed method is based on graph-based features and thus incorporates the relationships between pairwise feature samples into the feature selection process. Since the graph-based features are by nature completed weighted graphs, we use the steady state random walk to encapsulate the main characteristics of the graph-based features. Specifically, we compute a probability distribution of the walk visiting the vertices. Furthermore, we measure the discriminant power of each graph-based feature with respect to the target feature, through the Jensen-Shannon divergence measure between the probability distributions from the random walks. We select an optimal subset of features based on the most relevant graph-based features, through the Jensen-Shannon divergence measure. Unlike most existing state-of-the-art feature selection methods, the proposed method can accommodate both continuous and discrete target features. Experiments demonstrate the effectiveness and usefulness of the proposed feature selection algorithm on the problem of P2P lending platforms in China.

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. …

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.

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 facial expressions recognition (FER) is crucial to many applications. As technology advances and our needs evolve, compound emotion recognition is becoming increasingly important, along with basic emotion recognition. In the literature, Although, FER can be conducted primarily using multiple sensors. However, research shows that using facial images/videos to recognize facial expressions is better because visual presentation can convey more efficiently. Among state-of-the-art methods for FER systems, to improve the accuracy of the basic and compound FER systems, detection of facial action units (AUs) must be combined to detect basic and compound facial expressions. State-of-the-art results show that machine learning and deep learning-based approaches are more potent than conventional FER approaches. This paper surveys various learning frameworks for facial emotion recognition systems for detecting basic and compound emotions using the diverse database and summarizing state-of-the-art results to give good understanding of impact of each learning framework used in FER systems.

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.

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.

BackgroundPrevious reports have suggested impairment in facial expression recognition in delinquents, but controversy remains with respect to how such recognition is impaired. To address this issue, we investigated facial expression recognition in delinquents in detail.MethodsWe tested 24 male adolescent/young adult delinquents incarcerated in correctional facilities. We compared their performances with those of 24 age- and gender-matched control participants. Using standard photographs of facial expressions illustrating six basic emotions, participants matched each emotional facial expression with an appropriate verbal label.ResultsDelinquents were less accurate in the recognition of facial expressions that conveyed disgust than were control participants. The delinquents misrecognized the facial expressions of disgust as anger more frequently than did controls.ConclusionThese results suggest that one of the underpinnings of delinquency might be impaired recognition of emotional facial expressions, with a specific bias toward interpreting disgusted expressions as hostile angry expressions.

Facial Expression Recognition (FER), also known as Facial Emotion Recognition, is an active topic in computer vision and machine learning fields. This paper analyzes different feature extraction and classification methods to propose an efficient facial expression recognition system. We have studied several feature extraction methods, including Histogram of Oriented Gradients (HOG), face-encoding, and the features extracted by a VGG16 Network. For classification, different classical classifiers, including Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), and Logistic Regression, are evaluated with these features. Besides, we have trained a ResNet50 model from scratch and also tuned a ResNet50 which is pre-trained on VGGFace2 dataset. Finally, a part-based ensemble classifier is also proposed by focusing on different parts of face images. The experimental results provided on FER-2013 Dataset show that the tuned model of ResNet50 with a complete image of face, achieves higher performance than the other methods.

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.

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.

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