CAJ | 학술논문

人脸识别问题在很多情况下都会面临小训练样本的问题，在训练样本数量远小于数据维数的情形下许多人脸识别方法都会遇到困难。本文分析了造成小样本问题的原因，从虚拟样本扩充、数据降维以及算法优化等不同方面总结了解决方法，并对不同方法进行了简要评价，对解决小样本问题的未来发展方向进行了展望。
인검식별문제재흔다정황하도회면림소훈련양본적문제，재훈련양본수량원소우수거유수적정형하허다인검식별방법도회우도곤난。본문분석료조성소양본문제적원인，종허의양본확충、수거강유이급산법우화등불동방면총결료해결방법，병대불동방법진행료간요평개，대해결소양본문제적미래발전방향진행료전망。
Small Sample Size Problem is a general problem in face recognition. Most methods of face recognition will encounter difficulties when the number of samples is much smaller than the dimension of face data. This paper analyzed the the origin of the problem, and then defferent solutions were presented includeing virtual sample expansion,data reduction and algorithm optimization. A brief evaluation of different methods was given. Future directions of solving the problem were prospected .