Fingerprint Recognition: Enhancement, Feature Extraction and Automatic Evaluation of Algorithms

The identification of people by measuring some traits of individual anatomy or physiology has led to a specific research area called biometric recognition. This thesis is focused on improving fingerprint recognition systems considering three important problems: fingerprint enhancement, fingerprint orientation extraction and automatic evaluation of fingerprint algorithms. An effective extraction of salient fingerprint features depends on the quality of the input fingerprint. If the fingerprint is very noisy, we are not able to detect a reliable set of features. A new fingerprint enhancement method, which is both iterative and contextual, is proposed. This approach detects high-quality regions in fingerprints, selectively applies contextual filtering and iteratively expands like wildfire toward low-quality ones. A precise estimation of the orientation field would greatly simplify the estimation of other fingerprint features (singular points, minutiae) and improve the performance of a fingerprint recognition system. The fingerprint orientation extraction is improved following two directions. First, after the introduction of a new taxonomy of fingerprint orientation extraction methods, several variants of baseline methods are implemented and, pointing out the role of pre- and post- processing, we show how to improve the extraction. Second, the introduction of a new hybrid orientation extraction method, which follows an adaptive scheme, allows to improve significantly the orientation extraction in noisy fingerprints. Scientific papers typically propose recognition systems that integrate many modules and therefore an automatic evaluation of fingerprint algorithms is needed to isolate the contributions that determine an actual progress in the state-of-the-art. The lack of a publicly available framework to compare fingerprint orientation extraction algorithms, motivates the introduction of a new benchmark area called FOE (including fingerprints and manually-marked orientation ground-truth) along with fingerprint matching benchmarks in the FVC-onGoing framework. The success of such framework is discussed by providing relevant statistics: more than 1450 algorithms submitted and two international competitions.

Multimethodological study of molecular recognition phenomena

The study of the bio-recognition phenomena behind a biological process is nowadays considered a useful tool to deeply understand physiological mechanisms allowing the discovery of novel biological target and the development of new lead candidates. Moreover, understanding this kind of phenomena can be helpful in characterizing absorption, distribution, metabolism, elimination and toxicity properties of a new drug (ADMET parameters). Recent estimations show that about half of all drugs in development fail to make it to the market because of ADMET deficiencies; thus a rapid determination of ADMET parameters in early stages of drug discovery would save money and time, allowing to choose the better compound and to eliminate any losers. The monitoring of drug binding to plasma proteins is becoming essential in the field of drug discovery to characterize the drug distribution in human body. Human serum albumin (HSA) is the most abundant protein in plasma playing a fundamental role in the transport of drugs, metabolites and endogenous factors; so the study of the binding mechanism to HSA has become crucial to the early characterization of the pharmacokinetic profile of new potential leads. Furthermore, most of the distribution experiments carried out in vivo are performed on animals. Hence it is interesting to determine the binding of new compounds to albumins from different species to evaluate the reliability of extrapolating the distribution data obtained in animals to humans. It is clear how the characterization of interactions between proteins and drugs determines a growing need of methodologies to study any specific molecular event. A wide variety of biochemical techniques have been applied to this purpose. High-performance liquid affinity chromatography, circular dichroism and optical biosensor represent three techniques that can be able to elucidate the interaction of a new drug with its target and with others proteins that could interfere with ADMET parameters.