关键词: 人脸识别, 非线性距离, 夹角, 最近邻特征空间嵌入, 近邻类

Abstract:  Nearest feature space embedding (NFSE) method uses traditional Euclidean distance measure to select the nearest feature spaces, which causes within-class scatters and between-class scatters changing synchronously and cannot reflect the distribution of samples in the higher dimensional space accurately. Traversing all the classes when selecting the nearest feature space classes of every sample makes the training time long. To solve above problems, this paper proposes the nearest feature space embedding method based on nonlinear distance metric (NDNFSE), by using nonlinear distance formula to select the nearest feature spaces and using the nearest neighbor classifier combined with Euclidean distance and included angle between two samples to improve the recognition rate. NDNFSE only selects the nearest feature spaces within the nearest classes of every sample to save the training time. According to the experimental results, NDNFSE outperforms comparison algorithms for classification as a whole, with much shorter training time than that of NFSE.

Key words: face recognition, nonlinear distance, included angle, nearest feature space embedding, nearest classes