CENSUS HISTOGRAMS: A SIMPLE FEATURE EXTRACTION AND MATCHING APPROACH FOR FACE RECOGNITION

作     者：Chiachia, Giovani Marana, Aparecido Nilceu Ruf, Tobias Ernst, Andreas 

作者机构：Sao Paulo State Univ Dept Comp BR-17033360 Sao Paulo Brazil Fraunhofer Inst Integrated Circuits IIS Elect Imaging Dept D-91058 Erlangen Germany 

出 版 物：《INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE》 (国际图形识别与人工智能杂志)

年 卷 期：2011年第25卷第8期

页      面：1337-1348页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Face recognition census transform local binary patterns histogram matching feature extraction 

摘      要：Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance.