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• W2269066442 abstract "The development of security systems based on biometric features has been a topic of active research during the last three decades, because the recognition of the people identity to access control is a fundamental issue in these days. Terrorist attacks happened during the last decade have demonstrated that it is indispensable to have reliable security systems in offices, banks, airports, etc.; increasing in such way the necessity to develop more reliable methods for people recognition. The biometrics systems consist of a group of automated methods for recognition or verification of people identity using the physical characteristics or personal behavior of the person under analysis. In particular the face recognition has been a topic of active research because the face is the most direct way to recognize the people. In addition, the data acquisition of this method consists, simply, of taking a picture with or without collaboration of the person under analysis, doing it one of the biometric methods with larger acceptance among the users.The face recognition is a very complex activity of the human brain. For example, we can recognize hundred of faces learned throughout our life and to identify familiar faces at the first sight, even after several years of separation, with relative easy. However it is not a simple task for a computer. Thus to develop high performance face recognition systems, we must to develop accurate feature extraction and classification methods, because, as happens with any pattern recognition algorithm, the performance of a face recognition algorithm strongly depends on the feature extraction method and the classification systems used to carry out the face recognition task. Thus during the last decades several feature extraction methods for using in face recognition systems have been proposed during the last decades, which achieve high accurate recognition. Among the situations that drastically decrease the accuracy and that must be considered to develop high performance face recognition method we have: partial occlusion, illumination variations, size change, rotation and translation of the capture image, etc. To solve these problems several efficient feature extraction methods have been proposed, several of them using frequency domain transforms such as discrete Gabor transform, discrete Fourier transform, Discrete cosine transform, etc. These methods achieve recognition rates higher than 90%.In this talk, we analyze several frequency domain feature extraction methods based on the Discrete Gabor transform, Discrete Fourier Transform, Discrete Wavelet Transform, Discrete Cosine Transform, Discrete Walsh-Hadamard Transform and Eigenphases. These feature extraction methods are used with different classifiers such as artificial neural networks (ANN), Gaussian Mixture Models (GMM) and Support vector machines (SVM). The evaluation results were obtained using well known public domain databases such as AR Face Database." @default.
• W2269066442 created "2016-06-24" @default.
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• W2269066442 date "2010-10-04" @default.
• W2269066442 modified "2023-09-24" @default.
• W2269066442 title "Plenary lecture 1: face recognition using frequency domain feature extraction methods" @default.
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