Personal Recognition Using ICA
Independent Component Analysis (ICA) has been recently used to find representation of images with neurophysiological plausibility. Here we extended it to the problem of extract features suitable for personal identification from both face images and speech signal. A two-channel biometric system is presented in this paper. Both the face recognition module and voice recognition subsystem of it are built on the features extracted by ICA. Those two channels are integrated using a weighted geometric average assuming that face features and votce features are independent. Preliminary experimental results demonstrate a success of ICA in the application of biometric feature extraction. The integrated system overcomes the limitations of an identification system solely on faces or speeches and also gets improvement in performance.
Peilv Ding Xuelei Kang Liming Zhang
Dept.of Electronic Engineering Fudan University China, Shanghai, 200433
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
1217-1222
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）