Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face rec...Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.展开更多
A method for gazing-detection of human eyes using Support Vector Machine （SVM） based on statistic leaming theory （SLT） is proposed. According to the criteria of structural risk minimization of SVM,the errors betwe...A method for gazing-detection of human eyes using Support Vector Machine （SVM） based on statistic leaming theory （SLT） is proposed. According to the criteria of structural risk minimization of SVM,the errors between sample-data and model-data are minimized and the upper bound of predicting error of the model is also reduced. As a result,the generalization ability of the model is much improved. The simulation results show that, when limited training samples are used, the correct recognition rate of the tested samples can be as high as 100% ,which is much better than some previous results obtained by other methods. The higher processing speed enables the system to distinguish gazing or not-gazing in real-time.展开更多
文摘Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.
文摘A method for gazing-detection of human eyes using Support Vector Machine （SVM） based on statistic leaming theory （SLT） is proposed. According to the criteria of structural risk minimization of SVM,the errors between sample-data and model-data are minimized and the upper bound of predicting error of the model is also reduced. As a result,the generalization ability of the model is much improved. The simulation results show that, when limited training samples are used, the correct recognition rate of the tested samples can be as high as 100% ,which is much better than some previous results obtained by other methods. The higher processing speed enables the system to distinguish gazing or not-gazing in real-time.
