IEEE Trans Image Process - A novel eye localization method with rotation invariance.

Tópicos

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Resumo

This paper presents a novel learning method for precise eye localization, a challenge to be solved in order to improve the performance of face processing algorithms. Few existing approaches can directly detect and localize eyes with arbitrary angels in predicted eye regions, face images, and original portraits at the same time. To preserve rotation invariant property throughout the entire eye localization framework, a codebook of invariant local features is proposed for the representation of eye patterns. A heat map is then generated by integrating a 2-class sparse representation classifier with a pyramid-like detecting and locating strategy to fulfill the task of discriminative classification and precise localization. Furthermore, a series of prior information is adopted to improve the localization precision and accuracy. Experimental results on three different databases show that our method is capable of effectively locating eyes in arbitrary rotation situations (360? in plane).

Resumo Limpo

paper present novel learn method precis eye local challeng solv order improv perform face process algorithm exist approach can direct detect local eye arbitrari angel predict eye region face imag origin portrait time preserv rotat invari properti throughout entir eye local framework codebook invari local featur propos represent eye pattern heat map generat integr class spars represent classifi pyramidlik detect locat strategi fulfil task discrimin classif precis local furthermor seri prior inform adopt improv local precis accuraci experiment result three differ databas show method capabl effect locat eye arbitrari rotat situat plane

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