Thesis /

ROMDOC-THESIS-2016-433

Pattern recognition techniques for biometric technology

Mitrache, Ionuţ
2009-10-24

Abstract: This dissertation describes face recognition systems for both 2D and 3D approaches. For the 2D face recognition category we develop two systems, a feature-based and respectively a image-based algorithm. The 2D feature-based algorithm consists of following stages: preprocessing, extraction of 23 facial points corresponding to eyes, mouth and face boundary, feature representation by Gabor Wavelet Filtering, dimensionality reduction using Principal component analysis and neural classification using Concurent Self-Organizatation Maps (CSOM). The 2D image-base algorithm extracts the features using a variable size grid placed over the image. The features are represented as coefficients of the Gabor filters convoluted with the image on the grid nodes. Principal component analysis is applied to reduce the dimensionality of the feature vectors and the classification is realized based on two statistical classifiers, a nearest mean and respectively nearest neighbor classifier. The last described system is a 3D face recognition based on the depth-maps. The depth map of a 3D facial image is first thresholded to discard the background information. The detected face shape is normalized to a size of 20x200 pixels and the forefront nose point is selected to be the image center. Facial depth-values are scaled between 0 and 255 for translation and scaling-invariant identification. The features are represented by wavelet filtering and the classification is realized using a nearest mean, respectively nearest neighbor classifier. Experimental results show that the proposed method is able to identify subjects with different gesture and mimics in the presence of noise in their 3D facial images.

Keyword(s): Biometrie -- Teză de doctorat ; Tehnologie biometrică -- Teză de doctorat ; Recunoaşterea imaginilor -- Faţa umană -- Teză de doctorat ; Recunoaşterea formelor -- Teză de doctorat
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