3D Facial Feature Extraction and Recognition. An investigation of 3D face recognition: correction and normalisation of the facial data, extraction of facial features and classification using machine learning techniques.

Face recognition research using automatic or semi-automatic techniques has emerged over the last two decades. One reason for growing interest in this topic is the wide range of possible applications for face recognition systems. Another reason is the emergence of affordable hardware, supporting digital photography and video, which have made the acquisition of high-quality and high resolution 2D images much more ubiquitous. However, 2D recognition systems are sensitive to subject pose and illumination variations and 3D face recognition which is not directly affected by such environmental changes, could be used alone, or in combination with 2D recognition. Recently with the development of more affordable 3D acquisition systems and the availability of 3D face databases, 3D face recognition has been attracting interest to tackle the limitations in performance of most existing 2D systems. In this research, we introduce a robust automated 3D Face recognition system that implements 3D data of faces with different facial expressions, hair, shoulders, clothing, etc., extracts features for discrimination and uses machine learning techniques to make the final decision. A novel system for automatic processing for 3D facial data has been implemented using multi stage architecture; in a pre-processing and registration stage the data was standardized, spikes were removed, holes were filled and the face area was extracted. Then the nose region, which is relatively more rigid than other facial regions in an anatomical sense, was automatically located and analysed by computing the precise location of the symmetry plane. Then useful facial features and a set of effective 3D curves were extracted. Finally, the recognition and matching stage was implemented by using cascade correlation neural networks and support vector machine for classification, and the nearest neighbour algorithms for matching. It is worth noting that the FRGC data set is the most challenging data set available supporting research on 3D face recognition and machine learning techniques are widely recognised as appropriate and efficient classification methods

Robust signatures for 3D face registration and recognition

PhDBiometric authentication through face recognition has been an active area of research for the last few decades, motivated by its application-driven demand. The popularity of face recognition, compared to other biometric methods, is largely due to its minimum requirement of subject co-operation, relative ease of data capture and similarity to the natural way humans distinguish each other. 3D face recognition has recently received particular interest since three-dimensional face scans eliminate or reduce important limitations of 2D face images, such as illumination changes and pose variations. In fact, three-dimensional face scans are usually captured by scanners through the use of a constant structured-light source, making them invariant to environmental changes in illumination. Moreover, a single 3D scan also captures the entire face structure and allows for accurate pose normalisation. However, one of the biggest challenges that still remain in three-dimensional face scans is the sensitivity to large local deformations due to, for example, facial expressions. Due to the nature of the data, deformations bring about large changes in the 3D geometry of the scan. In addition to this, 3D scans are also characterised by noise and artefacts such as spikes and holes, which are uncommon with 2D images and requires a pre-processing stage that is speci c to the scanner used to capture the data. The aim of this thesis is to devise a face signature that is compact in size and overcomes the above mentioned limitations. We investigate the use of facial regions and landmarks towards a robust and compact face signature, and we study, implement and validate a region-based and a landmark-based face signature. Combinations of regions and landmarks are evaluated for their robustness to pose and expressions, while the matching scheme is evaluated for its robustness to noise and data artefacts

3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

Activity related biometrics for person authentication

One of the major challenges in human-machine interaction has always been the development of such techniques that are able to provide accurate human recognition, so as to other either personalized services or to protect critical infrastructures from unauthorized access. To this direction, a series of well stated and efficient methods have been proposed mainly based on biometric characteristics of the user. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the systems but also the intrusiveness of the collecting methods. The current thesis deals with the investigation of novel, activity-related biometric traits and their potential for multiple and unobtrusive authentication based on the spatiotemporal analysis of human activities. In particular, it starts with an extensive bibliography review regarding the most important works in the area of biometrics, exhibiting and justifying in parallel the transition that is performed from the classic biometrics to the new concept of behavioural biometrics. Based on previous works related to the human physiology and human motion and motivated by the intuitive assumption that different body types and different characters would produce distinguishable, and thus, valuable for biometric verification, activity-related traits, a new type of biometrics, the so-called prehension biometrics (i.e. the combined movement of reaching, grasping activities), is introduced and thoroughly studied herein. The analysis is performed via the so-called Activity hyper-Surfaces that form a dynamic movement-related manifold for the extraction of a series of behavioural features. Thereafter, the focus is laid on the extraction of continuous soft biometric features and their efficient combination with state-of-the-art biometric approaches towards increased authentication performance and enhanced security in template storage via Soft biometric Keys. In this context, a novel and generic probabilistic framework is proposed that produces an enhanced matching probability based on the modelling of the systematic error induced during the estimation of the aforementioned soft biometrics and the efficient clustering of the soft biometric feature space. Next, an extensive experimental evaluation of the proposed methodologies follows that effectively illustrates the increased authentication potential of the prehension-related biometrics and the significant advances in the recognition performance by the probabilistic framework. In particular, the prehension biometrics related biometrics is applied on several databases of ~100 different subjects in total performing a great variety of movements. The carried out experiments simulate both episodic and multiple authentication scenarios, while contextual parameters, (i.e. the ergonomic-based quality factors of the human body) are also taken into account. Furthermore, the probabilistic framework for augmenting biometric recognition via soft biometrics is applied on top of two state-of-art biometric systems, i.e. a gait recognition (> 100 subjects)- and a 3D face recognition-based one (~55 subjects), exhibiting significant advances to their performance. The thesis is concluded with an in-depth discussion summarizing the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

Nose tip localization on 2.5D facial models using differential geometry based point signatures and SVM classifier

International audienceNose tip localization on 2.5D facial models using differential geometry based point signatures and SVM classifie