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Báo cáo hóa học: " Feature Extraction Methods for Real-Time Face Detection and Classification"

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: Feature Extraction Methods for Real-Time Face Detection and Classification | EURASIP Journal on Applied Signal Processing 2005 13 2061-2071 2005 David Masip et al. Feature Extraction Methods for Real-Time Face Detection and Classification David Masip Centre de Visió per Computador CVC Departamento de Informatica Universitat Autònoma de Barcelona Bellaterra 08193 Spain Email davidm@cvc.uab.es Marco Bressan Centre de Visió per Computador CVC Departamento de Informatica Universitat Aut Onoma de Barcelona Bellaterra 08193 Spain Email marco.bressan@xrce.xerox.com Jordi Vitria Centre de Visió per Computador CVC Departamento de Informatica Universitat Aut Onoma de Barcelona Bellaterra 08193 Spain Email jordi@cvc.uab.es Received 22 December 2003 Revised 29 November 2004 We propose a complete scheme for face detection and recognition. We have used a Bayesian classifier for face detection and a nearest neighbor approach for face classification. To improve the performance of the classifier a feature extraction algorithm based on a modified nonparametric discriminant analysis has also been implemented. The complete scheme has been tested in a real-time environment achieving encouraging results. We also show a new boosting scheme based on adapting the features to the misclassified examples achieving also interesting results. Keywords and phrases face detection face recognition boosting feature extraction. 1. INTRODUCTION As computers become faster and faster new applications dealing with human faces become possible. Examples of this applications are face recognition applied to surveillance systems gesture analysis applied to user-friendly interfaces or gender recognition applied to reactive marketing. We will propose here a global face detection and recognition framework which has achieved good results in an uncontrolled environment. Usually working under uncontrolled conditions is one of the hardest problems of computer vision for example in applications where illumination presents strong changes or where we have to deal with objects under .