整合原始人脸图像和其虚拟样本的人脸分类算法

人脸识别作为最具吸引力的生物识别技术之一,由于会受到不同的照明条件、面部表情、姿态和环境的影响,仍然是一个具有挑战性的任务.众所周知,一幅人脸图像是对人脸的一次采样,它不应该被看作是脸部的绝对精确表示.然而在实际应用中很难获得足够多的人脸样本.随着稀疏表示方法在图像重建问题中的成功应用,研究人员提出了一种特殊的分类方法,即基于稀疏表示的分类方法.受此启发,提出了在稀疏表示框架下的整合原始人脸图像和虚拟样本的人脸分类算法.首先,通过合成虚拟训练样本来减少面部表示的不确定性.然后,在原始训练样本和虚拟样本组成的混合样本中通过计算来消除对分类影响较小的类别和单个样本,在系数分解的过程中采用最小误差正交匹配追踪(Error-Constrained Orthogonal Matching Pursuit,OMP)方法,进而选出贡献程度大的类别样本并进行分类.实验结果表明,提出的方法不仅能获得较高的人脸识别的精度,而且还具有更低的计算复杂性.

Integrating Original Images and its Virtual Samples for Face Recognition

As one of the most attractive biometric techniques,face recognition is still a challenging task.This is mainly owing to the varying lighting,facial expression,pose,and environment.In this sense,a face image is just an observation and it should not be considered as the absolutely accurate representation of the face.However,even in a real world face recognition system,it is difficult to obtain enough samples.The great success of sparse representation in image reconstruction triggers the research on sparse representation based pattern classification.Inspired by this,a sparse representation based classification method using category elimination and greedy search strategy was proposed for face recognition.First,we reduced the uncertainty of the face representation by synthesizing the virtual training samples.We applied an error-constrained orthogonal matching pursuit algorithm to exploit an optimal representation result of training samples from the classes by eliminating the category and the specific training samples.The final remaining training samples are used to produce a best representation of the test sample and to classify it.Then,we selected useful training samples that are similar to the test sample from the set of all the original and synthesized virtual training samples.Finally,we devised a representation approach based on the selected useful training samples to perform face recognition.Experimental results on five widely used face databases demonstrate that our proposed approach can not only obtain higher face recognition accuracy,but also has a lower computational complexity than the other state-of-the-art approaches.