CAJ | 학술논문

基于2D Gabor变换的人脸特征描述已经受到了很多人的关注.然而现有的Gabor特征维数较高,而且具有冗余性,因此选择最佳的Gabor特征用于人脸识别显得尤为的重要.利用最大余量原理的特征选择算法在目前的机器学习研究中已经占据了重要的地位.本文在基于余量的迭代搜索法(Simba)的基础上,引入了一种新的选择算法:基于余量的共轭梯度法(Cgmba),它只需较少次迭代就可以找到最佳解.我们在IMM人脸库上进行了实验,实验结果表明:尽管只使用了一半不到的特征,但Cgmba和Simba的识别率却分别提高了3.75和1.25个百分点,同时也证实了我们提出的Cgmba明显优于Simba.最后我们对Cgmba选择的Gabor特征的分布情况进行了分析,可以看出较大尺度的特征相对于较小尺度的特征对于分辩人脸的细微差别具有同等的重要性,而且在垂直,135°方向的特征具有更强的分辩能力.
기우2D Gabor변환적인검특정묘술이경수도료흔다인적관주.연이현유적Gabor특정유수교고,이차구유용여성,인차선택최가적Gabor특정용우인검식별현득우위적중요.이용최대여량원리적특정선택산법재목전적궤기학습연구중이경점거료중요적지위.본문재기우여량적질대수색법(Simba)적기출상,인입료일충신적선택산법:기우여량적공액제도법(Cgmba),타지수교소차질대취가이조도최가해.아문재IMM인검고상진행료실험,실험결과표명:진관지사용료일반불도적특정,단Cgmba화Simba적식별솔각분별제고료3.75화1.25개백분점,동시야증실료아문제출적Cgmba명현우우Simba.최후아문대Cgmba선택적Gabor특정적분포정황진행료분석,가이간출교대척도적특정상대우교소척도적특정대우분변인검적세미차별구유동등적중요성,이차재수직,135°방향적특정구유경강적분변능력.
Face representation based on 2D Gabor has attracted much attention. However, due to the fact that Gabor features currently are redundant and too high dimensional, selection of optimal Gabor features for face recognition appears to be paramount. Margin-based algorithms which use the large margin principle for feature selection have already played a crucial role in current machine learning research. In this paper, based on iterative search margin-based algorithm (Simba), we introduce a new selection algorithm: Conjugated gradient margin-based algorithm (Cgmba), which can find optimal solution at less iteration. Experiments were carried out on IMM face database. Results indicate that Cgmba and Simba can provide 3.75, 1.25 percent improvement in classification rate respectively, though less than half of all features are used. Moreover, superiority of our proposed approach to Simba is also demonstrated. Finally, the distribution of Gabor features selected by Cgmba is analyzed. It is inferred that the features in the larger scales have the same importance as those in the smaller scales in discriminating nuance of faces and features in vertical, and 135°orientations have more discriminative power.