面部识别系统
模式识别(心理学)
人工智能
线性判别分析
面子(社会学概念)
计算机科学
判别式
本征脸
核Fisher判别分析
主成分分析
特征提取
k-最近邻算法
降维
多重判别分析
作者
Xinli Cao,Ke Wen,Likun Huang,Bing Tang,Wei Zhang
摘要
Previous efforts on heterogeneous face recognition typically assume each subject has multiple training samples. However, this assumption may not hold in some special cases such as law-enforcement where only a Single Sample Per Person (SSPP) exists in the training set. For face recognition in SSPP scenario, it often suffers from overfitting and singular matrix problems. To solve this problem, we propose a novel learning-based algorithm called Coupled Discriminant Mapping (CDM) for heterogeneous face recognition. The CDM method finds a common space and learns a couple of discriminant projections for two different modalities without depending on the intra-class scatters. In the common space ,images of the same person are pulled into close proximity even if they come through different modalities meanwhile all the image under the same modality are pushed apart since each image belongs to a distinct class. The performance of CDM method is evaluated in two tasks: visual face image vs. near infrared face image and conventional face recognition. Experiments are conducted on two widely studied databases to show the effectiveness and consistence of the proposed CDM method.
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