Face Recognition Under Varying Illumination

This study is a result of a successful joint-venture with my adviser Prof. Dr. Muhittin Gökmen. I am thankful to him for his continuous assistance on preparing this project. Special thanks to the assistants of the Computer Vision Laboratory for their steady support and help in many topics related with the project

From Gabor Magnitude to Gabor Phase Features: Tackling the Problem of Face Recognition under Severe Illumination Changes

Among the numerous biometric systems presented in the literature, face recognition systems have received a great deal of attention in recent years. The main driving force in the development of these systems can be found in the enormous potential face recognition technology has in various application domains ranging from access control, human-machin

Improving Texture Categorization with Biologically Inspired Filtering

Within the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms. However, preprocessing techniques have received much less attention despite their important potential for improving the overall classification performance. We address this question by proposing a novel, simple, yet very powerful biologically-inspired filtering (BF) which simulates the performance of human retina. In the proposed approach, given a texture image, after applying a DoG filter to detect the "edges", we first split the filtered image into two "maps" alongside the sides of its edges. The feature extraction step is then carried out on the two "maps" instead of the input image. Our algorithm has several advantages such as simplicity, robustness to illumination and noise, and discriminative power. Experimental results on three large texture databases show that with an extremely low computational cost, the proposed method improves significantly the performance of many texture classification systems, notably in noisy environments. The source codes of the proposed algorithm can be downloaded from https://sites.google.com/site/nsonvu/code.Comment: 11 page

Homogeneous and Heterogeneous Face Recognition: Enhancing, Encoding and Matching for Practical Applications

Face Recognition is the automatic processing of face images with the purpose to recognize individuals. Recognition task becomes especially challenging in surveillance applications, where images are acquired from a long range in the presence of difficult environments. Short Wave Infrared (SWIR) is an emerging imaging modality that is able to produce clear long range images in difficult environments or during night time. Despite the benefits of the SWIR technology, matching SWIR images against a gallery of visible images presents a challenge, since the photometric properties of the images in the two spectral bands are highly distinct.;In this dissertation, we describe a cross spectral matching method that encodes magnitude and phase of multi-spectral face images filtered with a bank of Gabor filters. The magnitude of filtered images is encoded with Simplified Weber Local Descriptor (SWLD) and Local Binary Pattern (LBP) operators. The phase is encoded with Generalized Local Binary Pattern (GLBP) operator. Encoded multi-spectral images are mapped into a histogram representation and cross matched by applying symmetric Kullback-Leibler distance. Performance of the developed algorithm is demonstrated on TINDERS database that contains long range SWIR and color images acquired at a distance of 2, 50, and 106 meters.;Apart from long acquisition range, other variations and distortions such as pose variation, motion and out of focus blur, and uneven illumination may be observed in multispectral face images. Recognition performance of the face recognition matcher can be greatly affected by these distortions. It is important, therefore, to ensure that matching is performed on high quality images. Poor quality images have to be either enhanced or discarded. This dissertation addresses the problem of selecting good quality samples.;The last chapters of the dissertation suggest a number of modifications applied to the cross spectral matching algorithm for matching low resolution color images in near-real time. We show that the method that encodes the magnitude of Gabor filtered images with the SWLD operator guarantees high recognition rates. The modified method (Gabor-SWLD) is adopted in a camera network set up where cameras acquire several views of the same individual. The designed algorithm and software are fully automated and optimized to perform recognition in near-real time. We evaluate the recognition performance and the processing time of the method on a small dataset collected at WVU

Iris Recognition Using Scattering Transform and Textural Features

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%