%0 Journal Article %1 noauthororeditor %A "Jayanthi, T" %A Äji, S" %D 2014 %J Advances in Vision Computing: An International Journal (AVC) %K Face Feature Kernel analysis component extraction principal recognition %N 1 %P 9 %R 10.5121/avc.2014.1101 %T FACE RECOGNITION USING KERNEL PRINCIPAL COMPONENT ANALYSIS %U http://airccse.org/journal/avc/papers/1114avc01.pdf %V 1 %X Face recognition is attracting much attention in the society of network multimedia information access. Areas such as network security, content indexing and retrieval, and video compression benefits from face recognition technology because people are the center of attention in a lot of video. Network access control via face recognition not only makes hackers virtually impossible to steal one's password, but also increases the user-friendliness in human-computer interaction. The data in face images are distributed in a complex manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal Component Analysis (KPCA) is used to recognize the faces. A Gaussian model of skin segmentation method is applied here to exclude the global features such as beard, eyebrow, moustache, etc. both training and test images are randomly selected from four different data bases to improve the training. The experimental results show that the proposed framework is efficient for recognizing the humanfaces.

@article{noauthororeditor, abstract = {Face recognition is attracting much attention in the society of network multimedia information access. Areas such as network security, content indexing and retrieval, and video compression benefits from face recognition technology because people are the center of attention in a lot of video. Network access control via face recognition not only makes hackers virtually impossible to steal one's password, but also increases the user-friendliness in human-computer interaction. The data in face images are distributed in a complex manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal Component Analysis (KPCA) is used to recognize the faces. A Gaussian model of skin segmentation method is applied here to exclude the global features such as beard, eyebrow, moustache, etc. both training and test images are randomly selected from four different data bases to improve the training. The experimental results show that the proposed framework is efficient for recognizing the humanfaces.}, added-at = {2018-03-09T05:17:22.000+0100}, author = {"Jayanthi, T" and "Aji, S"}, biburl = {https://www.bibsonomy.org/bibtex/25fb51028c1121ec9067e7ac80775c7ae/vinston}, doi = {10.5121/avc.2014.1101}, interhash = {ecedb2d9c38502e8a9c2c6929c3be017}, intrahash = {5fb51028c1121ec9067e7ac80775c7ae}, journal = {Advances in Vision Computing: An International Journal (AVC)}, keywords = {Face Feature Kernel analysis component extraction principal recognition}, month = {March}, number = 1, pages = 9, timestamp = {2018-03-09T05:17:22.000+0100}, title = {FACE RECOGNITION USING KERNEL PRINCIPAL COMPONENT ANALYSIS}, url = {http://airccse.org/journal/avc/papers/1114avc01.pdf}, volume = 1, year = 2014 }