Efficient techniques for human face recognition

Pao Yue-kong Library Electronic Theses Database

Efficient techniques for human face recognition

 

Author: Pong, Kuong Hon
Title: Efficient techniques for human face recognition
Degree: Eng.D.
Year: 2012
Subject: Human face recognition (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Faculty of Engineering
Pages: xvii, 130 leaves : ill. (some col.) ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2554597
URI: http://theses.lib.polyu.edu.hk/handle/200/6869
Abstract: In this thesis, we investigate different efficient algorithms for face recognition, which has a wide range of applications. We first provide an overview of existing face recognition algorithms. Then, we devise efficient algorithms for face recognition, in particular when the input face image is of low resolution. For face recognition, image resolution is an important factor that has a great influence on the performance of a face recognition algorithm. In video surveillance, the face images concerned are usually of very low resolution. A traditional approach for low-resolution face recognition is to perform face interpolation or super-resolution, and then extract the useful features from the interpolated or super-resolved face images for recognition. To achieve a more efficient and accurate approach for low-resolution face recognition, we propose in this thesis a new method, namely "Gabor-Feature Hallucination", which predicts high-resolution Gabor features from the corresponding low-resolution Gabor features directly, by means of linear regression and Generalized Canonical Correlation Analysis. Then, the low-resolution features in the projected Generalized Canonical Correlation space and the predicted high-resolution Gabor features are adopted for face classification. This algorithm can avoid having to perform interpolation/super-resolution and then high-resolution Gabor feature extraction. Our algorithm estimates the high-resolution features, which are combined with the original low-resolution features to form an efficient feature using Canonical Correlation Analysis. Experimental results show that the proposed approach has a superior recognition rate and efficiency compared to the traditional methods.

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