This thesis is dedicated to extracting expression robust features for 3D face recognition. The use of 3D imaging enables the extraction of discriminative features that can significantly improve the recognition performance due to the availability of facial surface information such as depth, surface normals and curvature. Expression robust analysis using information from both depth and surface normals is investigated by dividing the main facial region into patches of different scales. The nasal region and adjoining parts of the cheeks are utilized as they are more consistent over different expressions and are hard to deliberately occlude. In addition, in comparison with other parts of the face, these regions have a high potential to produce discriminative features for recognition and overcome pose variations. An overview and classification methodology of the widely used 3D face databases are first introduced to provide an appropriate reference for 3D face database selection. Using the FRGC and Bosphorus databases, a low complexity pattern rejector for expression robust 3D face recognition is proposed by matching curves on the nasal and its environs, which results in a low-dimension feature set of only 60 points. To extract discriminative features more locally, a novel multi-scale and multi-component local shape descriptor is further proposed, which achieves more competitive performances under the identification and verification scenarios. In contrast with many of the existing work on 3D face recognition that consider captures obtained with laser scanners or structured light, this thesis also investigates applications to reconstructed 3D captures from lower cost photometric stereo imaging systems that have applications in real-world situations. To this end, the performance of the expression robust face recognition algorithms developed for captures from laser scanners are further evaluated on the Photoface database, which contains naturalistic expression variations. To improve the recognition performance of all types of 3D captures, a universal landmarking algorithm is proposed that makes uses of different components of the surface normals. Using facial profile signatures and thresholded surface normal maps, facial roll and yaw rotations are calibrated and five main landmarks are robustly detected on the well-aligned 3D nasal region. The landmarking results show that the detected landmarks demonstrate high within-class consistency and can achieve good recognition performances under different expressions. This is also the first landmarking work specifically developed for the reconstructed 3D captures from photometric stereo imaging systems.

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3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity.

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.

The purpose of this research was to enhance the methods towards the development of an efficient three dimensional face recognition system. More specifically, one of our aims was to investigate how the use of curvature of the diagonal profiles, extracted from 3D facial geometry models can help the neutral face recognition processes. Another aim was to use a gender classifier employed on 3D facial geometry in order to reduce the search space of the database on which facial recognition is performed. 3D facial geometry with facial expression possesses considerable challenges when it comes face recognition as identified by the communities involved in face recognition research. Thus, one aim of this study was to investigate the effects of the curvature-based method in face recognition under expression variations. Another aim was to develop techniques that can discriminate both expression-sensitive and expression-insensitive regions for ii face recognition based on non-neutral face geometry models. In the case of neutral face recognition, we developed a gender classification method using support vector machines based on the measurements of area and volume of selected regions of the face. This method reduced the search range of a database initially for a given image and hence reduces the computational time. Subsequently, in the characterisation of the face images, a minimum feature set of diagonal profiles, which we call T shape profiles, containing diacritic information were determined and extracted to characterise face models. We then used a method based on computing curvatures of selected facial regions to describe this feature set. In addition to the neutral face recognition, to solve the problem arising from data with facial expressions, initially, the curvature-based T shape profiles were employed and investigated for this purpose. For this purpose, the feature sets of the expression-invariant and expression-variant regions were determined respectively and described by geodesic distances and Euclidean distances. By using regression models the correlations between expressions and neutral feature sets were identified. This enabled us to discriminate expression-variant features and there was a gain in face recognition rate. The results of the study have indicated that our proposed curvature-based recognition, 3D gender classification of facial geometry and analysis of facial expressions, was capable of undertaking face recognition using a minimum set of features improving efficiency and computation.

This thesis is dedicated to exploring the potential of the 3D shape of the nasal region for face recognition. In comparison to other parts of the face, the nose has a number of distinctive features that make it attractive for recognition purposes. It is relatively stable over different facial expressions, easy to detect because of its salient convexity, and difficult to be intentionally cover up without attracting suspicion. In addition compared to other facial parts, such as forehead, chin, mouth and eyes, the nose is not vulnerable to unintentional occlusions caused by scarves or hair. Prior to undertaking a thorough analysis of the discriminative features of the 3D nasal regions, an overview of denoising algorithms and their impact on the 3D face recognition algorithms is first provided. This analysis, which is one of the first to address this issue, evaluates the performance of 3D holistic algorithms when various denoising methods are applied. One important outcome of this evaluation is to determine the optimal denoising parameters in terms of the overall 3D face recognition performance. A novel algorithm is also proposed to learn the statistics of the noise generated by the 3D laser scanners and then simulate it over the face point clouds. Using this process, the denoising and 3D face recognition algorithms’ robustness over various noise powers can be quantitatively evaluated. A new algorithm is proposed to find the nose tip from various expressions and self-occluded samples. Furthermore, novel applications of the nose region to align the faces in 3D is provided through two pose correction methods. The algorithms are very consistent and robust against different expressions, partial and self-occlusions. The nose’s discriminative strength for 3D face recognition is analysed using two approaches. The first one creates its feature sets by applying nasal curves to the depth map. The second approach utilises a novel feature space, based on histograms of normal vectors to the response of the Gabor wavelets applied to the nasal region. To create the feature spaces, various triangular and spherical patches and nasal curves are employed, giving a very high class separability. A genetic algorithm (GA) based feature selector is then used to make the feature space more robust against facial expressions. The basis of both algorithms is a highly consistent and accurate nasal region landmarking, which is quantitatively evaluated and compared with previous work. The recognition ranks provide the highest identification performance ever reported for the 3D nasal region. The results are not only higher than the previous 3D nose recognition algorithms, but also better than or very close to recent results for whole 3D face recognition. The algorithms have been evaluated on three widely used 3D face datasets, FRGC, Bosphorus and UMB-DB.

Human identification systems are commonly used for security issues. Most of them are based on ID card. However, using an ID card for identification may not be safe enough since people may not have any protection against the theft. Another solution to the identification problem is to use iris or fingerprints. However, systems based on the iris or fingerprints need close interaction to identification machine. Identifying someone from his photograph overcomes all these problems which can be called as face recognition. Common face recognition systems are based on the 2D image recognition but success rates of these methods are strictly depending on the environment. Variations on brightness and pose, complex background are the main problems for 2D image recognition systems. At this point, three dimensional face recognition techniques gain importance. Although there are a lot of methods developed for 3D face recognition, many of them assume that face is not rotated and there is not any destructive (i.e. beard, moustache, hair, hat, and eyeglasses) on the face. However, identification needs to be done though these destructives. Basic step for the face recognition is the determination of the anchor points (i.e. nose tip, inner eye points). In this study, the goal is to implement previously proposed four face recognition methods based on anchor point detection / &ldquo / Multimodal Facial Feature Extraction for Automatic 3D Face Recognition&rdquo / , &ldquo / Automatic Feature Extraction for Multiview 3D Face Recognition&rdquo / , &ldquo / Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression&rdquo / , &ldquo / 3D face detection using curvature analysis&rdquo / , to compare the success rates of them for rotated and destructed images and finally to propose improvements on these methods.

This thesis represents two approaches for three dimensional face recognition. In the first approach, a generic face model is fitted to human face. Local shape descriptors are located on the nodes of generic model mesh. Discriminative local shape descriptors on the nodes are selected and fed as input into the face recognition system. In the second approach, local shape descriptors which are uniformly distributed across the face are calculated. Among the calculated shape descriptors that are discriminative for recognition process are selected and used for three dimensional face recognition. Both approaches are tested with widely accepted FRGCv2.0 database and experiment protocol. Reported results are better than the state-of-theart systems. Recognition performances for neutral and non-neutral faces are also reported.

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.

The purpose of this research was to enhance the methods towards the development of an efficient three dimensional face recognition system. More specifically, one of our aims was to investigate how the use of curvature of the diagonal profiles, extracted from 3D facial geometry models can help the neutral face recognition processes. Another aim was to use a gender classifier employed on 3D facial geometry in order to reduce the search space of the database on which facial recognition is performed. 3D facial geometry with facial expression possesses considerable challenges when it comes face recognition as identified by the communities involved in face recognition research. Thus, one aim of this study was to investigate the effects of the curvature-based method in face recognition under expression variations. Another aim was to develop techniques that can discriminate both expression-sensitive and expression-insensitive regions for ii face recognition based on non-neutral face geometry models. In the case of neutral face recognition, we developed a gender classification method using support vector machines based on the measurements of area and volume of selected regions of the face. This method reduced the search range of a database initially for a given image and hence reduces the computational time. Subsequently, in the characterisation of the face images, a minimum feature set of diagonal profiles, which we call T shape profiles, containing diacritic information were determined and extracted to characterise face models. We then used a method based on computing curvatures of selected facial regions to describe this feature set. In addition to the neutral face recognition, to solve the problem arising from data with facial expressions, initially, the curvature-based T shape profiles were employed and investigated for this purpose. For this purpose, the feature sets of the expression-invariant and expression-variant regions were determined respectively and described by geodesic distances and Euclidean distances. By using regression models the correlations between expressions and neutral feature sets were identified. This enabled us to discriminate expression-variant features and there was a gain in face recognition rate. The results of the study have indicated that our proposed curvature-based recognition, 3D gender classification of facial geometry and analysis of facial expressions, was capable of undertaking face recognition using a minimum set of features improving efficiency and computation.