Dealing with variability factors and its applications to biometrics at a distance=Tratamiento de factores de vaiabilidad y su aplicación en biometría a distancia

Resumo

This Thesis is focused on dealing with variability factors in biometric recognition and applications of biometrics at a distance. In particular, this PhD Thesis explores the problem of variability factors assessment and how to deal with them by the incorporation of soft biometrics information in order to improve person recognition systems working at a distance. The proposed methods supported by experimental results show the benefits of adapting the system considering the variability of the sample at hand. Although being relatively young compared to other mature and long-used security technologies, biometrics have emerged in the last decade as a pushing alternative for applications where automatic recognition of people is needed. Certainly, biometrics are very attractive and useful for videosurveillance systems at a distance, widely distributed in our lifes, and for the final user: forget about PINs and passwords, you are your own key. However, we cannot forget that as any technology aimed to provide a security service, biometric systems should ensure a reliable performance in any scenario. Thus, it is of special relevance to understand and analyse the variability factors to which they are subjected in order to ensure a suitable performance and increase their benefits for the users. In this context, the present PhD Thesis gives an insight into the difficult problem of variability factors evaluation through the systematic study of biometric scenarios at a distance and the analysis of effective compensation methodologies that can minimize the effects of them. Pursuing the aim to increase the performance of the remote person recognition in this thriving technology. This way, the experimental studies presented in this Dissertation can help to further develop the ongoing variability compensation efforts, and may be used as guidelines to adapt the existing systems in biometric at a distance and make them more secure and stable. The problem of variability compensation in biometric systems had already been addressed in some previous works, but in most cases not using the acquisition distance related with the variability factors in order to identify and define scenarios. In this Dissertation, after summarizing and classifying the most relevant works related to the Thesis and defining what we understand as scenario at a distance, we describe methods applied throughout the experimental chapters. These experimental chapters are dedicated first to the study of variability factors (scenario analysis), and then to the application of the proposed techniques to deal with them (soft biometrics and adaptive fusion). All experiments are conducted using standard biometric data and benchmarks. The experimental part of the Thesis starts with a scenario evaluation of the variability factors found in face recognition systems. We evaluate, between others, the relationship between variability factors and the acquisition distance in this kind of systems, the variability of facial landmarks in mugshot and CCTV images, and the performance variability of different facial regions of the human face on various forensic scenarios at a distance. In addition to be useful background information that can guide and help experts to interpret and evaluate face evidences, findings can have a significant impact on the design of face recognition algorithms. We then study various types of soft biometric information available in biometrics at a distance suitable for videosurveillance and forensics applications. These soft labels can be visually identified at a distance by humans (or an automatic system) and their discriminative information will vary depending on the distance. It is worth noting that this relation between scenarios at a distance and the performance of soft biometrics for person recognition has not been studied in this way before. Moreover, the largest set of morphological facial soft biometric features extracted following forensic protocols is also introduced and evaluated. The experimental results using this set of features show that a system that is completely based on facial soft biometrics features for forensics is feasible. Finally, we study experimentally various types of adaptive fusion exploiting soft biometrics. In particular, we study: scenario-based, soft biometrics-based, facial regions-based, and color facial regions-based schemes of score-level fusion and their benefits in systems at a distance. The proposed adaptive fusion schemes achieve notable improvements demonstrating their utility in biometrics at a distance. The research work described in this Dissertation has led to novel contributions which include the development of two new methods to deal with variability factors in biometrics systems at a distance, namely: i) soft biometrics suitable for videosurveillance and forensics, and ii) adaptive fusion schemes at score-level based on scenario acquisition, soft biometrics, facial regions, and color facial regions. Moreover, different original experimental studies have been carried out during the development of the Thesis (e.g., relation between scenarios at a distance and variability factors). Besides, the research work completed throughout the Thesis includes the generation of various literature reviews and the generation of new biometric resources.