tailieunhanh - Báo cáo hóa học: " 2DPCA fractal features and genetic algorithm for efficient face representation and recognition"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: 2DPCA fractal features and genetic algorithm for efficient face representation and recognition | o EURASIP Journal on Information Security Ben Jemaa et al. EURASIP Journal on Information Security 2011 2011 1 http content 2011 1 1 a SpringerOpen Journal RESEARCH Open Access 2DPCA fractal features and genetic algorithm for efficient face representation and recognition Yousra Ben Jemaa Ahmed Derbel and Ahmed Ben Jmaa Abstract In this article we present an automatic face recognition system. We show that fractal features obtained from Iterated Function System allow a successful face recognition and outperform the classical approaches. We propose a new fractal feature extraction algorithm based on genetic algorithms to speed up the feature extraction step. In order to capture the more important information that is contained in a face with a few fractal features we use a bi-dimensional principal component analysis. We have shown with experimental results using two databases as to how the optimal recognition ratio and the recognition time make our system an effective tool for automatic face recognition. Keywords face recognition fractal coding 2DPCA IFS genetic algorithms I. Introduction The human face is a very rich source of information that can be used to identify persons. This ability of recognition allows us to distinguish persons despite the facial resemblance between them. Nowadays many researchers try to benefit from computer applications which become widely used in face automatic recognition. After more than 30 years of research we can classify the different existing face recognition systems into three main approaches. Local approaches which are based on the fact that the face contains parts that have a high discriminating power such as eyes nose mouth. To recognize a person we use either the blocks containing these regions or the geometric relationships between them 1 2 . Representative works include hidden Markov model 3 elastic bunch graph matching algorithm 4 . There are global approaches which treat the face as a whole object and