Guia docente 2023_24 Escola de Enxeñaría de Telecomunicación

Máster Universitario en Visión por Computador

Contents

Topic	Sub-topic
Basic principles of biometric identification	Identity versus biometric traits: Types of traits and biometric signatures. Variance intra-class and *nter-class of the biometric signatures. Influence of the sensors in the different signatures. Mathematical modelling of the biometric data: Extraction of characteristics. Compression. Representation versus Discrimination. Recognition, Identification, Verification and Authentication. Types of errors: TER, ERR, FAR, FRR.
Current biometric technologies	Physiological characteristics: fingerprints, iris, face, palm, retina, voice. Behavioural characteristics: signature (static and dynamic), keystrokes. Detection of alive sample. Pros and conts in the use of each biométric trait.
Facial recognition	Global technics (eigenfaces, fisherfaces) versus local technics (template matching, NCC, Elastic Bunch Graph Matching). The problem of the variation of illumination and pose. The problem of the detection and normalisation. Technicians of deep learning. Pros and cons.
Fingerprint recognition	Representation of minucias. Hausdorff distance. Gabor. filters. Tolerance to deformations. Types of sensors.
Iris recognition	Representation of the iris. Algorithm of Daugman. Algorithm of Wildes. Recognition at a distance. Pros and cons of iris recognition.
Multimodal recognition. Multibiometrics.	Combination of classifiers. Independent or correlated sources. Fusion of classifiers: intramodal, intermodal, algorithmic and scores-based. State of the art Systems using multimodal recognition and/or multibiometrics.