Personalized face and gesture analysis using hierarchical neural networks

The video-based computational analyses of human face and gesture signals encompass a myriad of challenging research problems involving computer vision, machine learning and human computer interaction. In this thesis, we focus on the following challenges: a) the classification of hand and body gestures along with the temporal localization of their occurrence in a continuous stream, b) the recognition of facial expressivity levels in people with Parkinson's Disease using multimodal feature representations, c) the prediction of student learning outcomes in intelligent tutoring systems using affect signals, and d) the personalization of machine learning models, which can adapt to subject and group-specific nuances in facial and gestural behavior. Specifically, we first conduct a quantitative comparison of two approaches to the problem of segmenting and classifying gestures on two benchmark gesture datasets: a method that simultaneously segments and classifies gestures versus a cascaded method that performs the tasks sequentially. Second, we introduce a framework that computationally predicts an accurate score for facial expressivity and validate it on a dataset of interview videos of people with Parkinson's disease. Third, based on a unique dataset of videos of students interacting with MathSpring, an intelligent tutoring system, collected by our collaborative research team, we build models to predict learning outcomes from their facial affect signals. Finally, we propose a novel solution to a relatively unexplored area in automatic face and gesture analysis research: personalization of models to individuals and groups. We develop hierarchical Bayesian neural networks to overcome the challenges posed by group or subject-specific variations in face and gesture signals. We successfully validate our formulation on the problems of personalized subject-specific gesture classification, context-specific facial expressivity recognition and student-specific learning outcome prediction. We demonstrate the flexibility of our hierarchical framework by validating the utility of both fully connected and recurrent neural architectures

A Survey on Gesture Pattern Recognition for Mute Peoples

These days data technology is developing. People are endeavoring to reduce their work by utilizing machines. The communication amongst human and computer ought to be convenient to the distinctive methods for communication are being searched. Utilization of hand gesture recognition is one of the methods for human-computer interaction. Gestures are for the most part of two types, static gestures and dynamic gestures. A large portion of the Research works have just concentrated on static gestures and in dynamic gestures they are having a few restrictions. We studied the writing on visual elucidation of hand gestures in the context of its part in Human Computer Interaction and different original works of researchers are underscored. The purpose for this review is to introduce the field of gesture recognition as a mechanism for interaction with computers

Chapter From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities

Interdisciplinary concept of dissipative soliton is unfolded in connection with ultrafast fibre lasers. The different mode-locking techniques as well as experimental realizations of dissipative soliton fibre lasers are surveyed briefly with an emphasis on their energy scalability. Basic topics of the dissipative soliton theory are elucidated in connection with concepts of energy scalability and stability. It is shown that the parametric space of dissipative soliton has reduced dimension and comparatively simple structure that simplifies the analysis and optimization of ultrafast fibre lasers. The main destabilization scenarios are described and the limits of energy scalability are connected with impact of optical turbulence and stimulated Raman scattering. The fast and slow dynamics of vector dissipative solitons are exposed

Real time facial expression recognition with AdaBoost

In this paper, we propose a novel method for facial expression recognition. The facial expression is extracted from human faces by an expression classifier that is learned from boosting Haar feature based Look-Up-Table type weak classifiers. The expression recognition system consists of three modules, face detection, facial feature landmark extraction and facial expression recognition. The implemented system can automatically recognize seven expressions in real time that include anger, disgust, fear, happiness, neutral, sadness and surprise. Experimental results are reported to show its potential applications in human computer interaction

Machine Analysis of Facial Expressions

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Toward an affect-sensitive multimodal human-computer interaction

The ability to recognize affective states of a person... This paper argues that next-generation human-computer interaction (HCI) designs need to include the essence of emotional intelligence -- the ability to recognize a user's affective states -- in order to become more human-like, more effective, and more efficient. Affective arousal modulates all nonverbal communicative cues (facial expressions, body movements, and vocal and physiological reactions). In a face-to-face interaction, humans detect and interpret those interactive signals of their communicator with little or no effort. Yet design and development of an automated system that accomplishes these tasks is rather difficult. This paper surveys the past work in solving these problems by a computer and provides a set of recommendations for developing the first part of an intelligent multimodal HCI -- an automatic personalized analyzer of a user's nonverbal affective feedback

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Statistical modelling for facial expression dynamics

PhDOne of the most powerful and fastest means of relaying emotions between humans are facial expressions. The ability to capture, understand and mimic those emotions and their underlying dynamics in the synthetic counterpart is a challenging task because of the complexity of human emotions, different ways of conveying them, non-linearities caused by facial feature and head motion, and the ever critical eye of the viewer. This thesis sets out to address some of the limitations of existing techniques by investigating three components of expression modelling and parameterisation framework: (1) Feature and expression manifold representation, (2) Pose estimation, and (3) Expression dynamics modelling and their parameterisation for the purpose of driving a synthetic head avatar. First, we introduce a hierarchical representation based on the Point Distribution Model (PDM). Holistic representations imply that non-linearities caused by the motion of facial features, and intrafeature correlations are implicitly embedded and hence have to be accounted for in the resulting expression space. Also such representations require large training datasets to account for all possible variations. To address those shortcomings, and to provide a basis for learning more subtle, localised variations, our representation consists of tree-like structure where a holistic root component is decomposed into leaves containing the jaw outline, each of the eye and eyebrows and the mouth. Each of the hierarchical components is modelled according to its intrinsic functionality, rather than the final, holistic expression label. Secondly, we introduce a statistical approach for capturing an underlying low-dimension expression manifold by utilising components of the previously defined hierarchical representation. As Principal Component Analysis (PCA) based approaches cannot reliably capture variations caused by large facial feature changes because of its linear nature, the underlying dynamics manifold for each of the hierarchical components is modelled using a Hierarchical Latent Variable Model (HLVM) approach. Whilst retaining PCA properties, such a model introduces a probability density model which can deal with missing or incomplete data and allows discovery of internal within cluster structures. All of the model parameters and underlying density model are automatically estimated during the training stage. We investigate the usefulness of such a model to larger and unseen datasets. Thirdly, we extend the concept of HLVM model to pose estimation to address the non-linear shape deformations and definition of the plausible pose space caused by large head motion. Since our head rarely stays still, and its movements are intrinsically connected with the way we perceive and understand the expressions, pose information is an integral part of their dynamics. The proposed 3 approach integrates into our existing hierarchical representation model. It is learned using sparse and discreetly sampled training dataset, and generalises to a larger and continuous view-sphere. Finally, we introduce a framework that models and extracts expression dynamics. In existing frameworks, explicit definition of expression intensity and pose information, is often overlooked, although usually implicitly embedded in the underlying representation. We investigate modelling of the expression dynamics based on use of static information only, and focus on its sufficiency for the task at hand. We compare a rule-based method that utilises the existing latent structure and provides a fusion of different components with holistic and Bayesian Network (BN) approaches. An Active Appearance Model (AAM) based tracker is used to extract relevant information from input sequences. Such information is subsequently used to define the parametric structure of the underlying expression dynamics. We demonstrate that such information can be utilised to animate a synthetic head avatar. Submitte

Face recognition using Hidden Markov Models

This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically