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 p...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.展开更多
This paper presents a novel method for multi classifier dynamic combination based on minimum cost criterion.Differentfrom common combination methods,the proposed dynamic combinationselectsthe mostsuitable group ofc...This paper presents a novel method for multi classifier dynamic combination based on minimum cost criterion.Differentfrom common combination methods,the proposed dynamic combinationselectsthe mostsuitable group ofclassifiersaccordingtothe Performance Predication Feature (PPF) extractedfrom theinputsample.PPFs arethefeatures ofsample thathave greatinfluence onthe performance ofclassifiers being studied.The decisionis made based on the criterion thatthe selected group ofclassifiers should minimize the cost caused by recognition errorand recognition time .Systematic methods for making this kind of combination is proposed and a practical example ofapplication is given.Because the adjustment ofcostfunction willresultin differenttrade off between recognitionrate and recognition speed,itis very convenientto satisfy different needs.The application in on line Chinese characterrecognitiontechnologyshowsthatthis kind ofcombination method hasthe merits ofhighflexibility and practicality,anditisindeed able toimprove the system performance .展开更多
文摘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.
文摘This paper presents a novel method for multi classifier dynamic combination based on minimum cost criterion.Differentfrom common combination methods,the proposed dynamic combinationselectsthe mostsuitable group ofclassifiersaccordingtothe Performance Predication Feature (PPF) extractedfrom theinputsample.PPFs arethefeatures ofsample thathave greatinfluence onthe performance ofclassifiers being studied.The decisionis made based on the criterion thatthe selected group ofclassifiers should minimize the cost caused by recognition errorand recognition time .Systematic methods for making this kind of combination is proposed and a practical example ofapplication is given.Because the adjustment ofcostfunction willresultin differenttrade off between recognitionrate and recognition speed,itis very convenientto satisfy different needs.The application in on line Chinese characterrecognitiontechnologyshowsthatthis kind ofcombination method hasthe merits ofhighflexibility and practicality,anditisindeed able toimprove the system performance .
