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.

Multifunctional nanocarriers encapsulating anti-Alzheimer drug for nasal delivery to central nervous system

Alzheimer's disease (AD) is a fatal neurodegenerative condition characterized clinically by progressive memory loss and irreversible cognitive deterioration. It has been shown that there is a progressive degeneration of the brain cholinergic neurons which leads to the appearance of cognitive symptoms of the disease. The aim of this work was the formulation of multifunctional nanocarriers for nasal administration of tacrine-HCl (THA). This route has many advantages; in particular is possible to convey the drug directly to the Central Nervous System, through the olfactory bulb. In particular, were prepared Albumin nanoparticles carrying beta cyclodextrin and two different beta cyclodextrin derivatives (hydroxypropyl beta cyclodextrin and sulphobutylether beta cyclodextrin), and Multifunctional liposomes, prepared using traditional excipients (cholesterol and phosphatidylcholine), partly enriched with α-tocopherol (Toc) and/or polyunsaturated fatty acids (eicosapentaenoic acid and docosahexaenoic acid) (Ω3). Both nanosystems were characterized in terms of size, Zeta potential and encapsulation efficiency. Were also evaluated their functional properties such as mucoadhesion and permeability, using an ex-vivo assay based on nasal sheep mucosa. On Liposomes were also assessed drug neuronal uptake, cell toxicity, antioxidant and, cytoprotective activity in the human neuronal cell line SH-SY5Y and finally tocopherol trans-membrane diffusion. Both the nanocarriers produced presented excellent properties and a high potential as new systems for CNS-delivery of anti-Alzheimer drugs via the nasal route.