Face recognition using sift features 论文
2009引用 264
Face and Expression RecognitionAdvanced Image and Video Retrieval TechniquesFace recognition and analysis
详细信息
- 发表日期
- 2009-11-01
- 发表年份
- 2009
关键词
Face and Expression RecognitionAdvanced Image and Video Retrieval TechniquesFace recognition and analysis
摘要
Scale Invariant Feature Transform (SIFT) has shown to be a powerful technique for general object recognition/detection. In this paper, we propose two new approaches: Volume-SIFT (VSIFT) and Partial-Descriptor-SIFT (PDSIFT) for face recognition based on the original SIFT algorithm. We compare holistic approaches: Fisherface (FLDA), the null space approach (NLDA) and Eigenfeature Regularization and Extraction (ERE) with feature based approaches: SIFT and PDSIFT. Experiments on the ORL and AR databases show that the performance of PDSIFT is significantly better than the original SIFT approach. Moreover, PDSIFT can achieve comparable performance as the most successful holistic approach ERE and significantly outperforms FLDA and NLDA.