基于独立负样本集和SVM的人脸确认算法

A Face Verification Algorithm Based on Negative Independent Sample Set and SVM

在许多人脸确认应用领域,例如人脸计算机安全登录系统中,没有用于SVM训练的人脸数据库可以提供,在现有基于SVM的人脸确认算法的基础上,根据实际应用的需求,提出了一种新的基于独立负样本集和SVM的人脸确认算法,该方法对注册的用户图像通过眼睛抖动的方法生成足够多的正样本,利用FLD技术进行特征提取,并利用基于Rank的一对多的识别方法去除同类项,解决了训练样本与负样本类别冲突问题·正负样本送SVM进行训练可以得到相应的SVM模型,对于待确认的人脸图像就可以采用SVM进行验证了·对SCUT人脸数据库的测试表明:足够数量的负样本能够保证较低的FAR,且支持向量的数量不会随着负样本集的数量增长而增长·应用这个算法,实现了一个计算机安全登录系统·

In many face verification applications, there are no face database for SVM training, such as PC face security logging-on system. A new face verification algorithm based on negative independent sample （NIS） set is presented, by analyzing existing SVM-based face verification algorithm and the demand for practical application. The approach generates enough positive samples by means of wobbling eyes of the user＇s registered image, employs FLD to extract feature, and deletes uniform samples in NIS with the rank-based FLD face recognition method. This scheme can resolves the classification conflict problem between the negative and the positive sample sets. After the negative and positive samples are sent to SVM for training, the SVM can do face verification for face image. The experiments on the SCUT face database indicate that the proposed method can ensure lower FAR if the negative samples are large enough, and the number of support vectors does not increase alone with the number of negative samples. A PC security logging-on system has been developed based on this face verification algorithm.