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Separated Same Rectangle Feature for Face Detection

Separated Same Rectangle Feature for Face Detection
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摘要 The paper proposes a new method of "Separated Same Rectangle Feature (SSRF)" for face detection. Generally, Haar-like feature is used to make an Adaboost training algorithm with strong classifier. Haar-like feature is composed of two or more attached same rectangles. Inefficiency of the Haar-like feature often results from two or more attached same rectangles. But the proposed SSRF are composed of two separated same rectangles. So, it is very flexible and detailed. Therefore it creates more accurate strong classifier than Haar-like feature. SSRF uses integral image to reduce execuive time. Haar-like feature calculates the Sanl of intmsities of pixels on two or more rectangles. But SSRF always calculates the stun of intensities of pixels on only two rectangles. The weak classifier of Ariaboost algorithm based on SSRF is fastex than one based on Haar-like feature. In the experiment, we use 1 000 face images and 1 000nm- face images for Adaboost training. The proposed SSRF shows about 0.9% higher acctwacy than Haar-like features.
出处 《Journal of Measurement Science and Instrumentation》 CAS 2010年第2期121-124,共4页 测试科学与仪器(英文版)
基金 supported by the Korea Research Foundation Grant funded by the Korean Government(MOEHRD),the MKE(The Ministry of Knowledge Economy,Korea) the ITRC(Information Technology Research Center)support program(NIPA-2009-(C1090-0902-0007))
关键词 seperated same rectangle feature Haar-like discreteadaboost FEATURE 人脸检测 矩形 Adaboost 测功 训练算法 计算强度 人脸图像 Haar
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参考文献6

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