IEEE Trans Image Process - Boosting color feature selection for color face recognition.

Tópicos

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Resumo

This paper introduces the new color face recognition (FR) method that makes effective use of boosting learning as color-component feature selection framework. The proposed boosting color-component feature selection framework is designed for finding the best set of color-component features from various color spaces (or models), aiming to achieve the best FR performance for a given FR task. In addition, to facilitate the complementary effect of the selected color-component features for the purpose of color FR, they are combined using the proposed weighted feature fusion scheme. The effectiveness of our color FR method has been successfully evaluated on the following five public face databases (DBs): CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that the results of the proposed method are impressively better than the results of other state-of-the-art color FR methods over different FR challenges including highly uncontrolled illumination, moderate pose variation, and small resolution face images.

Resumo Limpo

paper introduc new color face recognit fr method make effect use boost learn colorcompon featur select framework propos boost colorcompon featur select framework design find best set colorcompon featur various color space model aim achiev best fr perform given fr task addit facilit complementari effect select colorcompon featur purpos color fr combin use propos weight featur fusion scheme effect color fr method success evalu follow five public face databas dbs cmupi color feret xmvtsdb scface frgc experiment result show result propos method impress better result stateoftheart color fr method differ fr challeng includ high uncontrol illumin moder pose variat small resolut face imag

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