CAJ | 학술논문

为了能在正面人脸图像上对人眼位置进行检测和精确定位，提出了一种新颖高效的分级策略。利用Gabor变换计算显著极值图，得到若干具有最大显著极值的候选人眼区域；通过 PCA ( Principal Component Analysis)重构对候选区域进行验证，将具有最小重构误差的两个区域选定为眼睛区域；通过两级邻域运算对瞳孔进行精确定位。该方法对面部表情变化不敏感，同时具有非迭代和计算简单的优点。通过在JAFFE数据库上的对比实验，检测精度达到99.6%，验证了该方法的有效性。
위료능재정면인검도상상대인안위치진행검측화정학정위，제출료일충신영고효적분급책략。이용Gabor변환계산현저겁치도，득도약간구유최대현저겁치적후선인안구역；통과 PCA ( Principal Component Analysis)중구대후선구역진행험증，장구유최소중구오차적량개구역선정위안정구역；통과량급린역운산대동공진행정학정위。해방법대면부표정변화불민감，동시구유비질대화계산간단적우점。통과재JAFFE수거고상적대비실험，검측정도체도99.6%，험증료해방법적유효성。
We propose a novel and efficient hierarchical scheme, which can locate the accurate positions of the eyes from frontal face images. First, Gabor transform is used to calculate the salient map and a number of rectangular regions with the maximum saliency values are selected as the coarse eye-region candidates for further verification. Second, the two eye windows with the minimum PCA(Principal Component Analysis) reconstruction errors among the eye-candidate regions are selected. Finally, the pupil centers are localized by applying two neighborhood operators within the eye windows. The proposed algorithm is non-iterative, computationally simple and robust to different facial expressions. Experimental results on JAFFE database show that this algorithm can make the detection accuracy of 99 . 6%, and can achieve a superior performance compared to other state-of-the-art methods.