Analysis of image processing, machine techniques for contact-Less hand Biometrics
Autor: Belén Ríos Sánchez
Director: Carmen Sánchez Ávila
In this thesis different techniques of image processing, machine learning and information fusion have been analysed in relation to their applicability to contact-less hand biometrics. To this end, a modular and configurable system that explodes the multimodal nature of the hand to increase its robustness and accuracy has been designed, implemented and evaluated. Given the fact that different applications have different accuracy and time performance needs, the evaluation is aimed to provide a fair comparative of methods under different environmental conditions that can help to adapt the system to the specific requirements of a concrete final application.
A correct hand segmentation is necessary to extract reliable and invariant biometric features. For this reason, a comparative of different segmentation methods has been carried out, which includes well known methods such as global thresholding and graph cuts as well as a novelty flooding-based method that combines different imagebased segmentation approaches. These methods have been compared using various datasets of images that cover a wide spectrum of capturing conditions.
On the other hand, a comprehensive evaluation of different palmprint feature extraction methods comprising Gabor and Sobel filters, Local Binary Patterns, Local Derivative Patterns and Curvelets has been performed. Different parameter configurations have also been tested with the aim of finding out which arrangement provides the best result for each method. In addition to palmprint, also hand geometry features have been extracted. This evaluation includes also two different feature matching approaches: distance-based and Support Vector Machines.
Moreover, the feasibility of combining different feature extraction methods to yield into a more precise and robust multimodal solution has also been evaluated. Two different levels for fusing the biometric information have been compared: score-level and feature-level.
Finally, an evaluation methodology that allows for a fair comparison between different methods has been proposed. In particular, the proposed evaluation protocol is aimed to obtain an extensive evaluation of the system, which includes the assessment of the complete system under different environmental conditions as well as the testing of multiple combinations of methods for each module, providing a basis against which to compare future research.