Robust Face Recognition based on Color and Depth Information

One of the most important advantages of automatic human face recognition is its nonintrusiveness property. Face images can sometime be acquired without user's knowledge or explicit cooperation. However, face images acquired in an uncontrolled environment can appear with varying imaging conditions. Traditionally, researchers focus on tackling this problem using 2D gray-scale images due to the wide availability of 2D cameras and the low processing and storage cost of gray-scale data. Nevertheless, face recognition can not be performed reliably with 2D gray-scale data due to insu_cient information and its high sensitivity to pose, expression and illumination variations. Recent rapid development in hardware makes acquisition and processing of color and 3D data feasible. This thesis aims to improve face recognition accuracy and robustness using color and 3D information.In terms of color information usage, this thesis proposes several improvements over existing approaches. Firstly, the Block-wise Discriminant Color Space is proposed, which learns the discriminative color space based on local patches of a human face image instead of the holistic image, as human faces display different colors in different parts. Secondly, observing that most of the existing color spaces consist of at most three color components, while complementary information can be found in multiple color components across multiple color spaces and therefore the Multiple Color Fusion model is proposed to search and utilize multiple color components effectively. Lastly, two robust color face recognition algorithms are proposed. The Color Sparse Coding method can deal with face images with noise and occlusion. The Multi-linear Color Tensor Discriminant method harnesses multi-linear technique to handle non-linear data. Experiments show that all the proposed methods outperform their existing competitors.In terms of 3D information utilization, this thesis investigates the feasibility of face recognition using Kinect. Unlike traditional 3D scanners which are too slow in speed and too expensive in cost for broad face recognition applications, Kinect trades data quality for high speed and low cost. An algorithm is proposed to show that Kinect data can be used for face recognition despite its noisy nature. In order to fully utilize Kinect data, a more sophisticated RGB-D face recognition algorithm is developed which harnesses theColor Sparse Coding framework and 3D information to perform accurate face recognition robustly even under simultaneous varying conditions of poses, illuminations, expressionsand disguises

FRDF: face recognition using fusion of DTCWT and FFT features

Face recognition is a physiological Biometric trait widely used for personal authentication. In this paper we have proposed a technique for face recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Fast Fourier Transform (FFT) features. The Five Level DTCWT and FFT are applied on the pre-processed face image of size 128×512. The Five Level DTCWT features are arranged in a single column vector of size 384 × 1. The absolute values of FFT features are computed and arranged in column vector of size 65, 536 × 1. The DTCWT features are fused with dominant absolute FFT values using arithmetic addition to generate a final set of features. The test image features are compared with database features using Euclidean distance to identify a person. The face recognition is performed for different database such as ORL, JAFFE, L-SPACEK and CMU-PIE having different illumination and pose conditions. It is observed that the performance parameters False Acceptance Rate (FAR), False Rejection Rate (FRR) and True Success Rate (TSR) of proposed method FRDF: Face Recognition using Fusion of DTCWT and FFT are better compared to existing state of the art method

Face Recognition based on Oriented Complex Wavelets and FFT

The Face is a physiological Biometric trait used in Biometric System. In this paper face recognition using oriented complex wavelets and Fast Fourier Transform (FROCF) is proposed. The five-level Dual Tree Complex Wavelet Transform (DTCWT) is applied on face images to get shift invariant and directional features along±15o,±45o and±75o angular directions. The different pose, illumination and expression variations of face images are represented in frequency domain using Fast Fourier Transform (FFT) resulting in FFT features. Features of DTCWT and FFT are fused by arithmetic addition to get final features. Euclidean Distance classifier is applied to the features to recognize the genuine and imposter faces. The Performance analysis of proposed method is tested with ORL, JAFFE, L-SPACEK and CMU-PIE having different illumination and pose conditions. The Results shows that Recognition Rate of proposed FROCF is better compared to Existing Recognition Methods

Multimodal Hierarchical Face Recognition using Information from 2.5D Images

Facial recognition under uncontrolled acquisition environments faces major challenges that limit the deployment of real-life systems. The use of 2.5D information can be used to improve discriminative power of such systems in conditions where RGB information alone would fail. In this paper we propose a multimodal extension of a previous work, based on SIFT descriptors of RGB images, integrated with LBP information obtained from depth scans, modeled by an hierarchical framework motivated by principles of human cognition. The framework was tested on EURECOM dataset and proved that the inclusion of depth information improved significantly the results in all the tested conditions, compared to independent unimodal approaches

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Graph Inference with Applications to Low-Resource Audio Search and Indexing

The task of query-by-example search is to retrieve, from among a collection of data, the observations most similar to a given query. A common approach to this problem is based on viewing the data as vertices in a graph in which edge weights reflect similarities between observations. Errors arise in this graph-based framework both from errors in measuring these similarities and from approximations required for fast retrieval. In this thesis, we use tools from graph inference to analyze and control the sources of these errors. We establish novel theoretical results related to representation learning and to vertex nomination, and use these results to control the effects of model misspecification, noisy similarity measurement and approximation error on search accuracy. We present a state-of-the-art system for query-by-example audio search in the context of low-resource speech recognition, which also serves as an illustrative example and testbed for applying our theoretical results