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 featur...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.展开更多
The facial expression recognition systn using the Ariaboost based on the Split Rectangle feature is proposed in this paper. This system provides more various featmes in increasing speed and accuracy than the Haarolike...The facial expression recognition systn using the Ariaboost based on the Split Rectangle feature is proposed in this paper. This system provides more various featmes in increasing speed and accuracy than the Haarolike featrue of Viola, which is commonly used for the Adaboost training algorithm. The Split Rectangle feature uses the nmsk-like shape composed with 2 independent rectangles, instead of using mask-like shape of Haar-like feature, which is composed of 2 --4 adhered rectangles of Viola. Split Rectangle feature has less di- verged operation than the Haar-like feaze. It also requires less oper- ation because the stun of pixels requires ordy two rectangles. Split Rectangle feature provides various and fast features to the Adaboost, which produrces the strong classifier with increased accuracy and speed. In the experiment, the system had 5.92 ms performance speed and 84 %--94 % accuracy by leaming 5 facial expressions, neutral, happiness, sadness, anger and surprise with the use of the Adaboost based on the Split Rectangle feature.展开更多
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))
文摘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.
基金supported by the Brain Korea 21 Project in2010,the MKE(The Ministry of Knowledge Economy),Koreathe ITRC(Information Technology Research Center)support programsupervised by the NIPA(National ITIndustry Promotion Agency)(NI-PA-2010-(C1090-1021-0010))
文摘The facial expression recognition systn using the Ariaboost based on the Split Rectangle feature is proposed in this paper. This system provides more various featmes in increasing speed and accuracy than the Haarolike featrue of Viola, which is commonly used for the Adaboost training algorithm. The Split Rectangle feature uses the nmsk-like shape composed with 2 independent rectangles, instead of using mask-like shape of Haar-like feature, which is composed of 2 --4 adhered rectangles of Viola. Split Rectangle feature has less di- verged operation than the Haar-like feaze. It also requires less oper- ation because the stun of pixels requires ordy two rectangles. Split Rectangle feature provides various and fast features to the Adaboost, which produrces the strong classifier with increased accuracy and speed. In the experiment, the system had 5.92 ms performance speed and 84 %--94 % accuracy by leaming 5 facial expressions, neutral, happiness, sadness, anger and surprise with the use of the Adaboost based on the Split Rectangle feature.
