Design and Analysis of A New Illumination Invariant Human Face Recognition System

In this dissertation we propose the design and analysis of a new illumination invariant face recognition system. We show that the multiscale analysis of facial structure and features of face images leads to superior recognition rates for images under varying illumination. We assume that an image I ( x,y ) is a black box consisting of a combination of illumination and reflectance. A new approximation is proposed to enhance the illumination removal phase. As illumination resides in the low-frequency part of images, a high-performance multiresolution transformation is employed to accurately separate the frequency contents of input images. The procedure is followed by a fine-tuning process. After extracting a mask, feature vector is formed and the principal component analysis (PCA) is used for dimensionality reduction which is then proceeded by the extreme learning machine (ELM) as a classifier. We then analyze the effect of the frequency selectivity of subbands of the transformation on the performance of the proposed face recognition system. In fact, we first propose a method to tune the characteristics of a multiresolution transformation, and then analyze how these specifications may affect the recognition rate. In addition, we show that the proposed face recognition system can be further improved in terms of the computational time and accuracy. The motivation for this progress is related to the fact that although illumination mostly lies in the low-frequency part of images, these low-frequency components may have low- or high-resonance nature. Therefore, for the first time, we introduce the resonance based analysis of face images rather than the traditional frequency domain approaches. We found that energy selectivity of the subbands of the resonance based decomposition can lead to superior results with less computational complexity. The method is free of any prior information about the face shape. It is systematic and can be applied separately on each image. Several experiments are performed employing the well known databases such as the Yale B, Extended-Yale B, CMU-PIE, FERET, AT&T, and LFW. Illustrative examples are given and the results confirm the effectiveness of the method compared to the current results in the literature

Non-Standard Imaging Techniques

The first objective of the thesis is to investigate the problem of reconstructing a small-scale object (a few millimeters or smaller) in 3D. In Chapter 3, we show how this problem can be solved effectively by a new multifocus multiview 3D reconstruction procedure which includes a new Fixed-Lens multifocus image capture and a calibrated image registration technique using analytic homography transformation. The experimental results using the real and synthetic images demonstrate the effectiveness of the proposed solutions by showing that both the fixed-lens image capture and multifocus stacking with calibrated image alignment significantly reduce the errors in the camera poses and produce more complete 3D reconstructed models as compared with those by the conventional moving lens image capture and multifocus stacking. The second objective of the thesis is modelling the dual-pixel (DP) camera. In Chapter 4, to understand the potential of the DP sensor for computer vision applications, we study the formation of the DP pair which links the blur and the depth information. A mathematical DP model is proposed which can benefit depth estimation by the blur. These explorations motivate us to propose an end-to-end DDDNet (DP-based Depth and Deblur Network) to jointly estimate the depth and restore the image . Moreover, we define a reblur loss, which reflects the relationship of the DP image formation process with depth information, to regularize our depth estimate in training. To meet the requirement of a large amount of data for learning, we propose the first DP image simulator which allows us to create datasets with DP pairs from any existing RGBD dataset. As a side contribution, we collect a real dataset for further research. Extensive experimental evaluation on both synthetic and real datasets shows that our approach achieves competitive performance compared to state-of-the-art approaches. Another (third) objective of this thesis is to tackle the multifocus image fusion problem, particularly for long multifocus image sequences. Multifocus image stacking/fusion produces an in-focus image of a scene from a number of partially focused images of that scene in order to extend the depth of field. One of the limitations of the current state of the art multifocus fusion methods is not considering image registration/alignment before fusion. Consequently, fusing unregistered multifocus images produces an in-focus image containing misalignment artefacts. In Chapter 5, we propose image registration by projective transformation before fusion to remove the misalignment artefacts. We also propose a method based on 3D deconvolution to retrieve the in-focus image by formulating the multifocus image fusion problem as a 3D deconvolution problem. The proposed method achieves superior performance compared to the state of the art methods. It is also shown that, the proposed projective transformation for image registration can improve the quality of the fused images. Moreover, we implement a multifocus simulator to generate synthetic multifocus data from any RGB-D dataset. The fourth objective of this thesis is to explore new ways to detect the polarization state of light. To achieve the objective, in Chapter 6, we investigate a new optical filter namely optical rotation filter for detecting the polarization state with a fewer number of images. The proposed method can estimate polarization state using two images, one with the filter and another without. The accuracy of estimating the polarization parameters using the proposed method is almost similar to that of the existing state of the art method. In addition, the feasibility of detecting the polarization state using only one RGB image captured with the optical rotation filter is also demonstrated by estimating the image without the filter from the image with the filter using a generative adversarial network

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches