CAJ | 학술논문

提出了基于trace变换不变性特征的人脸识别算法，提高了单训练样本下姿势和表情变化后的识别率。应用一阶Scharr算子、二阶尺度适应的高斯型拉普拉斯算子（LOG）和Harris滤波器定位特征点，选择合适的泛函在特征点的邻域内进行trace变换得到具旋转和尺度不变性的特征描述子。根据特征描述子的特征向量和坐标值实现由粗到精的匹配，整个过程不涉及参数选择问题，保证了算法的稳定性。实验结果证明该算法降低了姿势和表情变化时识别率低的影响，并减少了算法运行时间。
제출료기우trace변환불변성특정적인검식별산법，제고료단훈련양본하자세화표정변화후적식별솔。응용일계Scharr산자、이계척도괄응적고사형랍보랍사산자（LOG）화Harris려파기정위특정점，선택합괄적범함재특정점적린역내진행trace변환득도구선전화척도불변성적특정묘술자。근거특정묘술자적특정향량화좌표치실현유조도정적필배，정개과정불섭급삼수선택문제，보증료산법적은정성。실험결과증명해산법강저료자세화표정변화시식별솔저적영향，병감소료산법운행시간。
A face recognition algorithm based on invariant feature of trace transform is presented to improve the recogni-tion rate of single training sample in variations of pose and expression. With application of one order Scharr operator and second order scale-adapted Laplacian of Gaussian(LoG)& Harris filter, feature points are detected. Proper functionals are chosen to perform trace transform in the circular region around each feature point so as to achieve feature descriptors which are invariant to rotation and scaling. A coarse-to-fine matching strategy is conducted by applying the descriptor’s feature vectors and coordinates. It guarantees the stability as no problem of parameter selection. The experimental results demonstrate that this method decreases the effect caused by the variations of pose and expression and also reduces the run-ning time.