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Báo cáo hóa học: " Optimization of Color Conversion for Face 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: Optimization of Color Conversion for Face Recognition | EURASIP Journal on Applied Signal Processing 2004 4 522-529 2004 Hindawi Publishing Corporation Optimization of Color Conversion for Face Recognition Creed F. Jones III Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA 24061-0111 USA Department of Computer Science Seattle Pacific University Seattle WA 98119-1957 USA Email crjones4@vt.edu A. Lynn Abbott Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Blacksburg VA 24061-0111 USA Email abbott@vt.edu Received 5 November 2002 Revised 16 October 2003 This paper concerns the conversion of color images to monochromatic form for the purpose of human face recognition. Many face recognition systems operate using monochromatic information alone even when color images are available. In such cases simple color transformations are commonly used that are not optimal for the face recognition task. We present a framework for selecting the transformation from face imagery using one of three methods Karhunen-Loeve analysis linear regression of color distribution and a genetic algorithm. Experimental results are presented for both the well-known eigenface method and for extraction of Gabor-based face features to demonstrate the potential for improved overall system performance. Using a database of 280 images our experiments using these methods resulted in performance improvements of approximately 4 to 14 . Keywords and phrases face recognition color image analysis color conversion Karhunen-Loeve analysis. 1. INTRODUCTION Most single-view face recognition systems operate using intensity monochromatic information alone. This is true even for systems that accept color imagery as input. The reason for this is not that multispectral data is lacking in information content but often because of practical considerations difficulties associated with illumination and color balancing for example as well as .