摘要
This paper presents a new technique of unified probabilistic models for facerecognition from only one single example image per person. The unified models, trained on anobtained training set with multiple samples per person, are used to recognize facial images fromanother disjoint database with a single sample per person. Variations between facial images aremodeled as two unified probabilistic models: within-class variations and between-class variations.Gaussian Mixture Models are used to approximate the distributions of the two variations and exploita classifier combination method to improve the performance. Extensive experimental results on theORL face database and the authors'' database (the ICT-JDL database) including totally 1,750 facialimages of 350 individuals demonstrate that the proposed technique, compared with traditionaleigenface method and some well-known traditional algorithms, is a significantly more effective androbust approach for face recognition.
This paper presents a new technique of unified probabilistic models for facerecognition from only one single example image per person. The unified models, trained on anobtained training set with multiple samples per person, are used to recognize facial images fromanother disjoint database with a single sample per person. Variations between facial images aremodeled as two unified probabilistic models: within-class variations and between-class variations.Gaussian Mixture Models are used to approximate the distributions of the two variations and exploita classifier combination method to improve the performance. Extensive experimental results on theORL face database and the authors'' database (the ICT-JDL database) including totally 1,750 facialimages of 350 individuals demonstrate that the proposed technique, compared with traditionaleigenface method and some well-known traditional algorithms, is a significantly more effective androbust approach for face recognition.
基金
国家自然科学基金，国家高技术研究发展计划(863计划)，四川成都宜城网络公司资助项目
