Learning local descriptors for face detection
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要In this paper, we propose a realtime face detection approach based on local structure and texture of the objects in gray-level images. Our strategy is to map the local spatial structures and image textures of face class into binary patterns, and use these binary patterns as local descriptors. Boosting based face detector is constructed using these local descriptors, and cascade scheme is employed to further improve the efficiency of the face detector. Compared to the existing face detection approaches, Our proposed method has two advantages: (1) it is robust to illumination changes to some extend, for the features use the information of local relationship instead of the original gray values; (2) the computational cost is very low, both in training procedure and evaluation step. The experimental results show that the proposed method can meet the demand of realtime applications with a satisfied detection performance.
著者Jin HL, Liu QS, Tang XO, Lu HQ
會議名稱IEEE International Conference on Multimedia and Expo (ICME)
會議開始日06.07.2005
會議完結日08.07.2005
會議地點Amsterdam
會議國家/地區荷蘭
出版年份2005
月份1
日期1
出版社IEEE
頁次929 - 932
國際標準書號0-7803-9331-7
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Computer Science, Information Systems

上次更新時間 2021-11-05 於 01:10