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