Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Sparsity Analysis for Computer Vision Applications

Ph.DDOCTOR OF PHILOSOPH

Face recognition in an unconstrained environment for monitoring student attendance

Traditional paper based attendance monitoring systems are time consuming and suscep- tible to both error and data loss. Where technical advances have attempted to solve the problem, they tend to improve only small portions i.e. confidence that data has been collected satisfactorily can be very high but technology can also be difficult to use, time consuming and impossible especially if the overall system is down. Camera based face recognition has the potential to resolve most monitoring problems. It is passive, easy and inexpensive to utilise; and if supported by a human safeguard can be very reliable. This thesis evaluates a strategy to monitor lecture attendance using images captured by cheap web cams in an unconstrained environment. A traditional recognition pipeline is utilised in which faces are automatically detected and aligned to a standard coordinate system before extracting Scale Invariant Feature Transform (SIFT), Local Binary Pattern (LBP) and Eigenface based features for classification. A greedy algorithm is employed to match captured faces to reference images with faces labelled and added to the training set over time. Performance is evaluated on images captured from a small lecture series over ten weeks. It is evident that performance improves during the series as new reference material is included within the training data. This correlation demonstrates that the success of the system is determined not only by the on-going capturing process but also the quality and variability of the initial training data. Whilst the system is capable of reasonable success, the experiments show that it also yields an unacceptably high false positive rate and cannot be used in isolation. This is primarily because the greedy nature of the algorithm allows the possibility of assigning multiple images of the same person captured in the same lecture to different students including ‘no shows’

New face recognition descriptor based on edge information for surgically-altered faces in uncontrolled environment

Since plastic surgery have increasingly become common in today’s society, existing face recognition systems have to deal with its effect on the features that characterizes a person’s facial identity. Its consequences on face recognition task are that the face images of an individual can turn out to be distinct and may tend towards resembling a different individual. Current research efforts mostly employ the intensity or texture based descriptors. However, with changes in skin-texture as a result of plastic surgery, the intensity or texture based descriptors may prove deficient since they enhance the texture differences between the pre-surgery and post-surgery images of the same individual. In this thesis, the effect of plastic surgery on facial features is modelled using affine operators. On the basis of the near-shape preserving property of the combination of the operators, the following assumption is made: The edge information is minimally influenced by plastic surgery. In order to exploit this information in real-world scenarios, it requires that face images be evenly illuminated. However, an evenly illuminated face image is far from reality on applying existing illumination normalization techniques. Thus, a new illumination normalization technique termed the rgb-Gamma Encoding (rgbGE) is proposed in this thesis. The rgbGE uses a fusion process to combine colour normalization and gamma correction, which are independently adapted to the face image from a new perspective. Subsequently, a new descriptor, namely the Local Edge Gradient Gabor Magnitude (LEGGM), is proposed. The LEGGM descriptor exploits the edge information to obtain intrinsic structural patterns of the face, which are ordinarily hidden in the original face pattern. These patterns are further embedded in the face pattern to obtain the complete face structural information. Then, Gabor encoding process is performed in order to accentuate the discriminative information of the complete face structural pattern. The resulting information is then learned using subspace learning models for effective representation of faces. Extensive experimental analysis of the designed face recognition method in terms of robustness and efficiency is presented with the aid of publicly available plastic surgery data set and other data sets of different cases of facial variation. The recognition performances of the designed face recognition method on the data sets show competitive and superior results over contemporary methods. Using a heterogeneous data set that typifies a real-world scenario, robustness against many cases of face variation is also shown with recognition performances above 90%

A Robust Face Recognition Algorithm for Real-World Applications

The proposed face recognition algorithm utilizes representation of local facial regions with the DCT. The local representation provides robustness against appearance variations in local regions caused by partial face occlusion or facial expression, whereas utilizing the frequency information provides robustness against changes in illumination. The algorithm also bypasses the facial feature localization step and formulates face alignment as an optimization problem in the classification stage

SPARSE RECOVERY BY NONCONVEX LIPSHITZIAN MAPPINGS

In recent years, the sparsity concept has attracted considerable attention in areas of applied mathematics and computer science, especially in signal and image processing fields. The general framework of sparse representation is now a mature concept with solid basis in relevant mathematical fields, such as probability, geometry of Banach spaces, harmonic analysis, theory of computability, and information-based complexity. Together with theoretical and practical advancements, also several numeric methods and algorithmic techniques have been developed in order to capture the complexity and the wide scope that the theory suggests. Sparse recovery relays over the fact that many signals can be represented in a sparse way, using only few nonzero coefficients in a suitable basis or overcomplete dictionary. Unfortunately, this problem, also called `0-norm minimization, is not only NP-hard, but also hard to approximate within an exponential factor of the optimal solution. Nevertheless, many heuristics for the problem has been obtained and proposed for many applications. This thesis provides new regularization methods for the sparse representation problem with application to face recognition and ECG signal compression. The proposed methods are based on fixed-point iteration scheme which combines nonconvex Lipschitzian-type mappings with canonical orthogonal projectors. The first are aimed at uniformly enhancing the sparseness level by shrinking effects, the latter to project back into the feasible space of solutions. In the second part of this thesis we study two applications in which sparseness has been successfully applied in recent areas of the signal and image processing: the face recognition problem and the ECG signal compression problem

Robust approaches for face recognition

This thesis gave answers to a number of important questions regarding face classification. Via this research, new methods were introduced to represent four facial attributes (three of them related to the demographic information of the human face: gender, age and race) and the fourth one related to facial expression. It stated that, discriminative facial features regarding to demographic information (gender, age and race) and expression information can be obtained by applying texture analysis techniques to the polar raster sampled images. In addition, it is found that, multi-label classification (MLC) is more suitable in the real world as a human face can be associated with multiple labels