Machine Learning Methods for Human Identification from Dorsal Hand Images

Person identification is a process that uniquely identifies an individual based on physical or behavioural traits. This study investigates methods for the analysis of images of the human hand, focusing on their uniqueness and potential use for human identification. The human hand has significant and distinctive characteristics, and is highly complex and interesting, yet it has not been explored in much detail, particularly in the context of the contemporary high level of digitalisation and, more specifically, the advances in artificial intelligence (AI), machine learning (ML) and computer vision (CV). This research area is highly multi-disciplinary, involving anatomists, anthropologists, bioinformaticians, image analysts and, increasingly, computer scientists. A growing pool of advanced methods based on AI, ML and CV can benefit and relate directly to a better representation of the human hand in computer analysis. Historically, the research methods in this area relied on ‘handcrafted’ features such as the local binary pattern (LBP) and histogram of gradient (HOG) extraction, which necessitated human intervention. However, such approaches struggle to encode the human hand in variable conditions effectively, because of the change in camera viewpoint, hand pose, rotation, image quality, and self-occlusion. Thus, their performance is limited. Recently, there has been a surge of interest in deep learning neural network (DLNN) approaches, specifically convolutional neural networks (CNNs), due to the highly accurate results achieved in many applications and the wide availability of images. This work investigates advanced methods based on ML and DLNN for segmenting hand images with various rotation changes into different patches (e.g., knuckles and fingernails). The thesis focuses on developing ML methods like pre-trained CNN models on the 'ImageNet' dataset to learn the underlying structure of the human hand by extracting robust features from hand images with diverse conditions of rotation, and image quality. Also, this study investigates fine-tuning the pre-trained models of DLNN on subsets of hand images, as well as using various similarity metrics to find the best match of the individual’s hand. Furthermore, this work explores different types of ensemble learning or fusions, those of different region and similarity metrics to improve human identification results. This thesis also presents a study of a Siamese network on sub-images or segments of human dorsal hands for identification tasks. All presented approaches are compared with the state-of-the-art methods. This study advances the understanding of variations in and the uniqueness of humans using patches of their hands (e.g., different types of knuckles and fingernails). Lastly, it compares the matching performances of the left- and right-hand patches using various hand datasets and investigates whether the fingernail produces better identification results than the knuckles. This research shows that the proposed framework for person identification based on hand components led to better person identification results. The framework consists of vital feature extractions based on deep learning neural network (DLNN) and similarity metrics. These elements enhanced the performance. Also, the fingernails' shape performed better than other hand components, including the base, major, and minor knuckles. The left hand can be more distinguishable to individuals than the right hand. The fine-tuning of the hand components and ensemble learning improved the identification results

Robust steganographic techniques for secure biometric-based remote authentication

Biometrics are widely accepted as the most reliable proof of identity, entitlement to services, and for crime-related forensics. Using biometrics for remote authentication is becoming an essential requirement for the development of knowledge-based economy in the digital age. Ensuring security and integrity of the biometric data or templates is critical to the success of deployment especially because once the data compromised the whole authentication system is compromised with serious consequences for identity theft, fraud as well as loss of privacy. Protecting biometric data whether stored in databases or transmitted over an open network channel is a serious challenge and cryptography may not be the answer. The main premise of this thesis is that Digital Steganography can provide an alternative security solutions that can be exploited to deal with the biometric transmission problem. The main objective of the thesis is to design, develop and test steganographic tools to support remote biometric authentication. We focus on investigating the selection of biometrics feature representations suitable for hiding in natural cover images and designing steganography systems that are specific for hiding such biometric data rather than being suitable for general purpose. The embedding schemes are expected to have high security characteristics resistant to several types of steganalysis tools and maintain accuracy of recognition post embedding. We shall limit our investigations to embedding face biometrics, but the same challenges and approaches should help in developing similar embedding schemes for other biometrics. To achieve this our investigations and proposals are done in different directions which explain in the rest of this section. Reviewing the literature on the state-of-art in steganography has revealed a rich source of theoretical work and creative approaches that have helped generate a variety of embedding schemes as well as steganalysis tools but almost all focused on embedding random looking secrets. The review greatly helped in identifying the main challenges in the field and the main criteria for success in terms of difficult to reconcile requirements on embedding capacity, efficiency of embedding, robustness against steganalysis attacks, and stego image quality. On the biometrics front the review revealed another rich source of different face biometric feature vectors. The review helped shaping our primary objectives as (1) identifying a binarised face feature factor with high discriminating power that is susceptible to embedding in images, (2) develop a special purpose content-based steganography schemes that can benefit from the well-defined structure of the face biometric data in the embedding procedure while preserving accuracy without leaking information about the source biometric data, and (3) conduct sufficient sets of experiments to test the performance of the developed schemes, highlight the advantages as well as limitations, if any, of the developed system with regards to the above mentioned criteria. We argue that the well-known LBP histogram face biometric scheme satisfies the desired properties and we demonstrate that our new more efficient wavelet based versions called LBPH patterns is much more compact and has improved accuracy. In fact the wavelet version schemes reduce the number of features by 22% to 72% of the original version of LBP scheme guaranteeing better invisibility post embedding. We shall then develop 2 steganographic schemes. The first is the LSB-witness is a general purpose scheme that avoids changing the LSB-plane guaranteeing robustness against targeted steganalysis tools, but establish the viability of using steganography for remote biometric-based recognition. However, it may modify the 2nd LSB of cover pixels as a witness for the presence of the secret bits in the 1st LSB and thereby has some disadvantages with regards to the stego image quality. Our search for a new scheme that exploits the structure of the secret face LBPH patterns for improved stego image quality has led to the development of the first content-based steganography scheme. Embedding is guided by searching for similarities between the LBPH patterns and the structure of the cover image LSB bit-planes partitioned into 8-bit or 4-bit patterns. We shall demonstrate the excellent benefits of using content-based embedding scheme in terms of improved stego image quality, greatly reduced payload, reduced lower bound on optimal embedding efficiency, robustness against all targeted steganalysis tools. Unfortunately our scheme was not robust against the blind or universal SRM steganalysis tool. However we demonstrated robustness against SRM at low payload when our scheme was modified by restricting embedding to edge and textured pixels. The low payload in this case is sufficient to embed a secret full face LBPH patterns. Our work opens new exciting opportunities to build successful real applications of content-based steganography and presents plenty of research challenges

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state