Face recognition using sift features

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 holisti...

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Bibliographic Details
Published in2009 16th IEEE International Conference on Image Processing (ICIP) pp. 3313 - 3316
Main Authors Cong Geng, Xudong Jiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Face detection
Face recognition
Feature extraction
Humans
Null space
Object detection
Object recognition
Power engineering and energy
Robustness
Spatial databases
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ISBN9781424456536
1424456533
ISSN1522-4880
DOI10.1109/ICIP.2009.5413956

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Summary: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.
ISBN:9781424456536
1424456533
ISSN:1522-4880
DOI:10.1109/ICIP.2009.5413956