CAJ | 학술논문

人脸识别是一种前沿的生物识别技术，作为脸部的重要特征，通过对人眼的定位，可以获取标准人脸，有效地提高人脸识别率。针对变化光照条件下人眼定位变得困难的问题，实现了一种人眼定位方法。首先通过retinex理论获取光照不变量；其次，利用边缘直方图描述符(EHD)得到人眼候选区；再次，使用支持向量机(SVM)和人眼概率分布( EPM )来确定人眼精确位置。为了进一步提高速度，提出一种基于haar-like特征的双眼筛选法，该方法和EHD构成多级筛选策略，大量减少了非人眼区域，加快了SVM的分类速度。实验表明，改进后的方法在保证较高检测率的同时具有较少的计算时间。
인검식별시일충전연적생물식별기술，작위검부적중요특정，통과대인안적정위，가이획취표준인검，유효지제고인검식별솔。침대변화광조조건하인안정위변득곤난적문제，실현료일충인안정위방법。수선통과retinex이론획취광조불변량；기차，이용변연직방도묘술부(EHD)득도인안후선구；재차，사용지지향량궤(SVM)화인안개솔분포( EPM )래학정인안정학위치。위료진일보제고속도，제출일충기우haar-like특정적쌍안사선법，해방법화EHD구성다급사선책략，대량감소료비인안구역，가쾌료SVM적분류속도。실험표명，개진후적방법재보증교고검측솔적동시구유교소적계산시간。
Face recognition is an advanced biology recognition technology. As an important feature on human face, eye positions are determined to generate normalized face so as to improve face recognition accuracy effectively. This paper implements an eye location to solve the eye location problem under varying illumination. Firstly,we utilize retinex theory to extract illumination invariant. Secondly, an edge histogram descriptor ( EHD) is used to obtain eye candidate detection on illumination on normalized image. At last, we employ the support vector machine ( SVM ) and eye probability map ( EPM ) to accurately locate eyes. In order to improve computation speed of the method, a multi-level selection strategy which consists of a binocular selection method based on haar-like feature and the EHD is proposed. This approach is able to reduce a plenty of non-eye areas to increase the speed of the SVM. Experiment demonstrates that the proposed method can achieve high detection accuracy and low consumption time at the same time.