Paper
24 December 2013 A combined SIFT/SURF descriptor for automatic face recognition
Ladislav Lenc, Pavel Král
Author Affiliations +
Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90672C (2013) https://doi.org/10.1117/12.2052804
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom
Abstract
This paper deals with Automatic Face Recognition (AFR). A novel approach which combines the SIFT and SURF features for the face representation is proposed. The obtained combined SIFT/SURF descriptor is then used for face comparison by the adapted Kepenekci matching method. The proposed method is evaluated on the FERET and CTK corpora. The obtained recognition rates are 98.4% and 64.6% respectively. These recognition scores show that our approach outperforms significantly all other methods on these corpora. The differences between recognition error rates of the proposed approach and the second best one are 41% and 7% in relative value respectively.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ladislav Lenc and Pavel Král "A combined SIFT/SURF descriptor for automatic face recognition", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90672C (24 December 2013); https://doi.org/10.1117/12.2052804
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CITATIONS
Cited by 6 scholarly publications and 1 patent.
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KEYWORDS
Facial recognition systems

Image filtering

Detection and tracking algorithms

Gaussian filters

Databases

Feature extraction

Sensors

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