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

Face recognition technologies for evidential evaluation of video traces

Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future

3D Face Reconstruction from Light Field Images: A Model-free Approach

Reconstructing 3D facial geometry from a single RGB image has recently instigated wide research interest. However, it is still an ill-posed problem and most methods rely on prior models hence undermining the accuracy of the recovered 3D faces. In this paper, we exploit the Epipolar Plane Images (EPI) obtained from light field cameras and learn CNN models that recover horizontal and vertical 3D facial curves from the respective horizontal and vertical EPIs. Our 3D face reconstruction network (FaceLFnet) comprises a densely connected architecture to learn accurate 3D facial curves from low resolution EPIs. To train the proposed FaceLFnets from scratch, we synthesize photo-realistic light field images from 3D facial scans. The curve by curve 3D face estimation approach allows the networks to learn from only 14K images of 80 identities, which still comprises over 11 Million EPIs/curves. The estimated facial curves are merged into a single pointcloud to which a surface is fitted to get the final 3D face. Our method is model-free, requires only a few training samples to learn FaceLFnet and can reconstruct 3D faces with high accuracy from single light field images under varying poses, expressions and lighting conditions. Comparison on the BU-3DFE and BU-4DFE datasets show that our method reduces reconstruction errors by over 20% compared to recent state of the art

2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA

We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for both dimension reduction and good recognition performance when compared to the complete Gabor filter bank. The system has been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and achieved average recognition rate of 98.9 %

3D Face Recognition: Feature Extraction Based on Directional Signatures from Range Data and Disparity Maps

In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation