期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2016
卷号：14
期号：2A
页码：114-119
DOI：10.12928/telkomnika.v14i2A.4371
语种：English
出版社：Universitas Ahmad Dahlan
摘要：As the internet keeping developing, face recognition has become a research hotspot in the field of biometrics. This paper proposes an improved face recognition algorithm that reduces the influence of illumination and posture variations. First, face images are transformed by using the improved contourlet transform method to get low frequency sub-band images and high frequency sub-band images. Then this paper uses the principal component analysis to extract main features. Finally, combines these statistic features together as feature vector and recognize face images. Analysis, experiment and proof on the ORL face database and the Yale face database show that this algorithm is better able to recognize faces, reduce the influence of illumination and posture variations and increase the efficiency of face recognition.
其他摘要：As the internet keeping developing, face recognition has become a research hotspot in the field of biometrics. This paper proposes an improved face recognition algorithm that reduces the influence of illumination and posture variations. First, face images are transformed by using the improved contourlet transform method to get low frequency sub-band images and high frequency sub-band images. Then this paper uses the principal component analysis to extract main features. Finally, combines these statistic features together as feature vector and recognize face images. Analysis, experiment and proof on the ORL face database and the Yale face database show that this algorithm is better able to recognize faces, reduce the influence of illumination and posture variations and increase the efficiency of face recognition.
关键词：contourlet transform; principal component analysis; face recognition
