Automatic Face Recognition System Based on Local Fourier-Bessel Features

We present an automatic face verification system inspired by known properties of biological systems. In the proposed algorithm the whole image is converted from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT). Using the whole image is compared to the case where only face image regions (local analysis) are considered. The resulting representations are embedded in a dissimilarity space, where each image is represented by its distance to all the other images, and a Pseudo-Fisher discriminator is built. Verification test results on the FERET database showed that the local-based algorithm outperforms the global-FBT version. The local-FBT algorithm performed as state-of-the-art methods under different testing conditions, indicating that the proposed system is highly robust for expression, age, and illumination variations. We also evaluated the performance of the proposed system under strong occlusion conditions and found that it is highly robust for up to 50% of face occlusion. Finally, we automated completely the verification system by implementing face and eye detection algorithms. Under this condition, the local approach was only slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI

A Subspace Projection Methodology for Nonlinear Manifold Based Face Recognition

A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature space is developed. Least squares estimation approach that utilizes interdependency between points in training patterns is used to form the nonlinear region. The proposed feature extraction strategy is employed to improve face recognition accuracy under varying illumination conditions and facial expressions. Though the face features show variations under these conditions, the features of one individual tend to cluster together and can be considered as a neighborhood. Low dimensional representations of face patterns in the feature space may lie in a nonlinear constraint region, which when modeled leads to efficient pattern classification. A feature space encompassing multiple pattern classes can be trained by modeling a separate constraint region for each pattern class and obtaining a mean constraint region by averaging all the individual regions. Unlike most other nonlinear techniques, the proposed method provides an easy intuitive way to place new points onto a nonlinear region in the feature space. The proposed feature extraction and classification method results in improved accuracy when compared to the classical linear representations. Face recognition accuracy is further improved by introducing the concepts of modularity, discriminant analysis and phase congruency into the proposed method. In the modular approach, feature components are extracted from different sub-modules of the images and concatenated to make a single vector to represent a face region. By doing this we are able to extract features that are more representative of the local features of the face. When projected onto an arbitrary line, samples from well formed clusters could produce a confused mixture of samples from all the classes leading to poor recognition. Discriminant analysis aims to find an optimal line orientation for which the data classes are well separated. Experiments performed on various databases to evaluate the performance of the proposed face recognition technique have shown improvement in recognition accuracy, especially under varying illumination conditions and facial expressions. This shows that the integration of multiple subspaces, each representing a part of a higher order nonlinear function, could represent a pattern with variability. Research work is progressing to investigate the effectiveness of subspace projection methodology for building manifolds with other nonlinear functions and to identify the optimum nonlinear function from an object classification perspective

Face Recognition Methodologies Using Component Analysis: The Contemporary Affirmation of The Recent Literature

This paper explored the contemporary affirmation of the recent literature in the context of face recognition systems, a review motivated by contradictory claims in the literature. This paper shows how the relative performance of recent claims based on methodologies such as PCA and ICA, which are depend on the task statement. It then explores the space of each model acclaimed in recent literature. In the process, this paper verifies the results of many of the face recognition models in the literature, and relates them to each other and to this work

Supervised extraction of face subspaces based on multimodal discriminant analysis

Master'sMASTER OF SCIENC

3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach

Facial landmark detection on 3D human faces has had numerous applications in the literature such as establishing point-to-point correspondence between 3D face models which is itself a key step for a wide range of applications like 3D face detection and authentication, matching, reconstruction, and retrieval, to name a few. Two groups of approaches, namely knowledge-driven and data-driven approaches, have been employed for facial landmarking in the literature. Knowledge-driven techniques are the traditional approaches that have been widely used to locate landmarks on human faces. In these approaches, a user with sucient knowledge and experience usually denes features to be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage of machine learning algorithms to detect prominent features on 3D face models. Besides the key advantages, each category of these techniques has limitations that prevent it from generating the most reliable results. In this work we propose to combine the strengths of the two approaches to detect facial landmarks in a more ecient and precise way. The suggested approach consists of two phases. First, some salient features of the faces are extracted using expert systems. Afterwards, these points are used as the initial control points in the well-known Thin Plate Spline (TPS) technique to deform the input face towards a reference face model. Second, by exploring and utilizing multiple machine learning algorithms another group of landmarks are extracted. The data-driven landmark detection step is performed in a supervised manner providing an information-rich set of training data in which a set of local descriptors are computed and used to train the algorithm. We then, use the detected landmarks for establishing point-to-point correspondence between the 3D human faces mainly using an improved version of Iterative Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for 3D face matching applications

3D face recognition using photometric stereo

Automatic face recognition has been an active research area for the last four decades. This thesis explores innovative bio-inspired concepts aimed at improved face recognition using surface normals. New directions in salient data representation are explored using data captured via a photometric stereo method from the University of the West of England’s “Photoface” device. Accuracy assessments demonstrate the advantage of the capture format and the synergy offered by near infrared light sources in achieving more accurate results than under conventional visible light. Two 3D face databases have been created as part of the thesis – the publicly available Photoface database which contains 3187 images of 453 subjects and the 3DE-VISIR dataset which contains 363 images of 115 people with different expressions captured simultaneously under near infrared and visible light. The Photoface database is believed to be the ?rst to capture naturalistic 3D face models. Subsets of these databases are then used to show the results of experiments inspired by the human visual system. Experimental results show that optimal recognition rates are achieved using surprisingly low resolution of only 10x10 pixels on surface normal data, which corresponds to the spatial frequency range of optimal human performance. Motivated by the observed increase in recognition speed and accuracy that occurs in humans when faces are caricatured, novel interpretations of caricaturing using outlying data and pixel locations with high variance show that performance remains disproportionately high when up to 90% of the data has been discarded. These direct methods of dimensionality reduction have useful implications for the storage and processing requirements for commercial face recognition systems. The novel variance approach is extended to recognise positive expressions with 90% accuracy which has useful implications for human-computer interaction as well as ensuring that a subject has the correct expression prior to recognition. Furthermore, the subject recognition rate is improved by removing those pixels which encode expression. Finally, preliminary work into feature detection on surface normals by extending Haar-like features is presented which is also shown to be useful for correcting the pose of the head as part of a fully operational device. The system operates with an accuracy of 98.65% at a false acceptance rate of only 0.01 on front facing heads with neutral expressions. The work has shown how new avenues of enquiry inspired by our observation of the human visual system can offer useful advantages towards achieving more robust autonomous computer-based facial recognition

Groupwise non-rigid registration for automatic construction of appearance models of the human craniofacial complex for analysis, synthesis and simulation

Finally, a novel application of 3D appearance modelling is proposed: a faster than real-time algorithm for statistically constrained quasi-mechanical simulation. Experiments demonstrate superior realism, achieved in the proposed method by employing statistical appearance models to drive the simulation, in comparison with the comparable state-of-the-art quasi-mechanical approaches.EThOS - Electronic Theses Online ServiceGBUnited Kingdo