CAJ | 학술논문

为了提高多姿态人耳的识别准确率，提出了一种结合梯度方向直方图（HOG）特征和监督保局投影的人耳识别方法。人耳图像被划分成重叠的子区域，在每个子区域中计算局部HOG特征。所有的局部 HOG特征构造成包含丰富信息的高维向量，高维向量映射到流形空间利用监督保局投影进行鉴别分析，获得强鉴别力的特征。利用最小欧氏分类器进行分类识别。结果表明，本文方法提高了大姿态人耳的识别率。稀疏的子区域表示和局部 HOG特征能在一定程度上克服姿态变化造成的对齐误差，对角度变化有很好的鲁棒性。
위료제고다자태인이적식별준학솔，제출료일충결합제도방향직방도（HOG）특정화감독보국투영적인이식별방법。인이도상피화분성중첩적자구역，재매개자구역중계산국부HOG특정。소유적국부 HOG특정구조성포함봉부신식적고유향량，고유향량영사도류형공간이용감독보국투영진행감별분석，획득강감별력적특정。이용최소구씨분류기진행분류식별。결과표명，본문방법제고료대자태인이적식별솔。희소적자구역표시화국부 HOG특정능재일정정도상극복자태변화조성적대제오차，대각도변화유흔호적로봉성。
To improve recognition accuracy of multi-pose ear ,a novel method is presented which combines histograms of oriented gradients (HOG) feature with supervised locality preserving projection (SLPP) .The ear image is first represented by a set of overlapped sub-regions ,and then the local HOG features are computed on each sub-region on account of its invariance to local geometric transformation . The local HOG features are constructed as a high dimensional vector containing ample information ,and then the high dimensional vector is mapped to manifold space to acquire stronger discriminant feature using SLPP .Finally ,the minimum European distance classifier is used for recognition .Experimental results show that a higher recognition rate is achieved by the proposed method than by other related methods .The feature representation of the sparse sub-region and SLPP can compensate for the alignment errors caused by pose variations ,and the recognition method can efficiently improve the robustness of pose variation .