计算机科学 ›› 2009, Vol. 36 ›› Issue (8): 285-287.

• 图形图像及体系结构 • 上一篇    下一篇

基于局部和全局的特征提取算法及在人脸识别中的应用

张国印,楼宋江,程慧杰,王庆军   

  1. (哈尔滨工程大学计算机科学与技术学院 哈尔滨 150001)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Feature Extraction Algorithm Based on Locality and Globality and its Application in Face Recognition

GHANG Guo-yin, LOU Song-jiang, CHENG Hui-jie, WANG Qing-jun   

  • Online:2018-11-16 Published:2018-11-16

摘要: 提出了一种基于局部和全局特征的特征提取算法。该算法不仅能保持数据集的局部性,同时也考虑了数据集的全局性,使得降维后的数据既能保持部近关系,又能从整体上较好地重构和展现。PCA()能较好地展现原数据集,LPP能保持局部部近关系,算法结合了这两个算法的思想,但由于LPP没有考虑类别信息,故先对LPP进行改进,给出了一种有监督的局部保持投影算法,使得提出的算法能更加有利于分类问题。通过人脸识别实验,验证了算法的正确性和有效性。

关键词: 特征提取,局部性,全局性,LPP,主成分分析算法,人脸识别

Abstract: A feature extraction algorithm based on locality and globality was proposed, which on one hand takes the locality of the data into consideration, on the other hand takes the globality of data into account. Consequently, the data afto dimension reduction not only preserves the locality relationship, but also reconstructs and represents the original dato perfectly. PCA (Principal Component Analysis) can represent the data nicely and LPP (Locality Preserving Projection) can preserve the locality relationship, so the algorithm just hybrid the two of them. But LPP does not utilize the classification information, so first LPP-based algorithm is improved, a supervised version is given, which results in that the algorithm is more suitable for classification task. Experiments in face recognition validate the correctness and effectiveness of the proposed algorithm.

Key words: Feature extraction, Locality, Globality, LPP, PCA, Face recognition

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