Subspace Learning with Enriched Databases Using Symmetry

Feature extraction has been extensively investigated and discussed in computer vision and pattern recognition literature over the past three decades. It has particularly attracted more and more attention in recent years due to the increasing demands for developing real-world human computer interaction systems. While a large number of feature extraction algorithms have been proposed in the literature and some of them have achieved reasonably good performance in many real world applications, such as face recognition, gait recognition, facial expression recognition and human age estimation, there is still some room for further improvement to address the challenges of these methods. In this thesis, we propose various enhanced linear subspace algorithms and apply them to face and gait feature extraction to demonstrate their efficacy and superiority over state-of-the-art methods. Specifically, we propose four new subspace learning approaches, including double weighted subspace learning, parametric regularized subspace learning, cost-sensitive subspace learning, and subspace learning with limited number of training samples. Lastly, we also apply multi-label subspace learning techniques for human age estimation. The above-mentioned methods have been successfully applied to several computer vision applications, such as face recognition, facial expression recognition and gait-based human age estimation. Experimental results are presented to demonstrate the efficacy of the proposed methods.

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.

Face recognition technology has evolved as an enchanting solution to address the contemporary needs in order to perform identification and verification of identity claims. By advancing the feature extraction methods and dimensionality reduction techniques in the application of pattern recognition, a number of face recognition systems has been developed with distinct degrees of success. Locality preserving projection (LPP) is a recently proposed method for unsupervised linear dimensionality reduction. LPP preserve the local structure of face image space which is usually more significant than the global structure preserved by principal component analysis (PCA) and linear discriminant analysis (LDA). This paper focuses on a systematic analysis of locality-preserving projections and the application of LPP in combination with an existing technique This combined approach of LPP through MPCA can preserve the global and the local structure of the face image which is proved very effective. Proposed approach is tested using the AT & T face database. Experimental results show the significant improvements in the face recognition performance in comparison with some previous 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.

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.

Face recognition has motivated several research studies in the last years owing not only to its applicability and multidisciplinary inherent characteristics, but also to its important role in human relationship. Despite extensive studies on face recognition, a number of related problems has still remained challenging in this research topic. It is well known that humans can overcome any computer program in the task of face recognition when artefacts are present such as changes in pose, illumination, occlusion, aging and etc. For instance, young children can robustly identify their parents, friends and common social groups without any previous explicit teaching or learning. Some recent research in Neuroscience (Kandel et al., 2000; Bakker et al., 2008) has shown that there is some new information about how humans deal with such high dimensional and sparse visual recognition task, indicating that the brain does not memorize all details of the visual stimuli (images) to perform face recognition (Brady et al., 2008). Instead, our associative memory tends to work essentially on the most expressive information (Bakker et al., 2008; Oja, 1982). In fact, theoretical models (Treves and Rolls, 1994; O’Reilly and Rudy, 2001; Norman and O’Reilly, 2003) have indicated that the ability of our memory relies on the capability of orthogonalizing (pattern separation) and completing (pattern prototyping) partial patterns in order to encode, store and recall information (O’Reily and McClelland, 1994; Kuhl et al., 2010). Therefore, subspace learning techniques have a close biological inspiration and reasonability in terms of computational methods to possibly exploring and understanding the human behaviour of recognizing faces. The aim of this chapter is to study the non-supervised subspace learning called SelfOrganizing Map (SOM) (Kohonen, 1982; Kohonen, 1990) based on the principle of prototyping face image observations. Our idea with this study is not only to seek a low dimensional Euclidean embedding subspace of a set of face samples that describes the intrinsic similarities of the data (Kitani et al., 2006; Giraldi et al., 2008; Thomaz et al., 2009; Kitani et al., 2010), but also to explore an alternative mapping representation based on manifold models topologically constrained.

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.

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

Subspace learning techniques are among the most popular methods for face recognition. In this paper, we propose a novel face recognition technique for two dimensional subspace learning which is able to exploit the symmetry nature of human faces. We extent the Two Dimensional Clustering based Discriminant Analysis (2DCDA) by incorporating an appropriate symmetry regularizer into its objective function in order to determine symmetric projection vectors. The proposed Symmetric Two Dimensional Clustering based Discriminant Analysis technique has been applied to the face recognition problem. Experimental results showed that the proposed technique achieves better classification performance in comparison to the standard one.

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. …

This paper describes in detail about 2D and 3D face and video face databases developed in the year from 1992 to 2009. The still face database discussed here includes: Our Laboratory Database of Faces (ORL), Yale face database, Yale face databaseB(B stands for Big), Yale face database extendedB, FERET(Face Recognition Technology) face database and CASPEAL is a large scale Chinese face database with varying PEAL(Pose Expression Accessory, and Lighting). The 3D face databases like MIT-CBCL (Centre for Biological and Computational Learning) face recognition database, Georgia Tech face database, Cohn-Kanade Action Unit Face database and 3D BFM (Basel Face Model) are also discussed in this paper. In CK AU, CK+AU(Cohn-Kanade Action Unit) coded facial expression database, a 3D model is developed from a 2D model which is obtained from the captured video of different subjects. The other Face databases and development of new Face databases are discussed at the end of this paper. The application of proposed concept is to develop a new small size face database which includes all the important criteria included in the previous face and video face databases.

Facial expression interpretation, recognition and analysis is a key issue in visual communication and man to machine interaction. We address the issues of facial expression recognition and synthesis and compare the proposed bilinear factorization based representations with previously investigated methods such as linear discriminant analysis and linear regression. We conclude that bilinear factorization outperforms these techniques in terms of correct recognition rates and synthesis photorealism especially when the number of training samples is restrained.

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

Face alignment and face recognition are two fundamental problems in the facial analysis community. For face alignment, it forms the basis for the accurate face recognition, age estimation, and facial expression recognition. For face recognition, it has been widely applied in various practical scenarios such as access control system, massive surveillance, human computer interaction, etc.. There mainly exist two lines of works in these two fields, namely holistic face alignment and recognition, and partial face alignment and recognition. Numerous holistic face alignment and recognition works