Weighted SIFT Feature Learning with Hamming Distance for Face Recognition

Guoyu Lu, Yingjie Hu, Chandra Kambhamettu


Scale-invariant feature transform (SIFT) feature has been successfully utilized for face recognition for its tolerance to the changes of image scaling, rotation and distortion. However, a big concern on the use of original SIFT feature for face recognition is SIFT feature’s high dimensionality which leads to slow image matching. Meanwhile, large memory capacity is required to store high dimensional SIFT features. Aiming to find an efficient approach to solve these issues, we propose a new integrated method for face recognition in this paper. The new method consists of two novel functional modules in which a projection function transforms the original SIFT features into a low dimensional Hamming feature space while each bit of the Hamming descriptor is ranked based on their discrimination power. Furthermore, a weighting function assigns different weights to the correctly matched features based on their matching times. Our proposed face recognition method has been applied on two benchmark facial image datasets: ORL and Yale datasets. The experimental results have shown that the new method is able to produce good image recognition rate with much improved computational speed.


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Paper Citation

in Harvard Style

Lu G., Hu Y. and Kambhamettu C. (2014). Weighted SIFT Feature Learning with Hamming Distance for Face Recognition . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 691-699. DOI: 10.5220/0004859806910699

in Bibtex Style

author={Guoyu Lu and Yingjie Hu and Chandra Kambhamettu},
title={Weighted SIFT Feature Learning with Hamming Distance for Face Recognition},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},

in EndNote Style

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Weighted SIFT Feature Learning with Hamming Distance for Face Recognition
SN - 978-989-758-004-8
AU - Lu G.
AU - Hu Y.
AU - Kambhamettu C.
PY - 2014
SP - 691
EP - 699
DO - 10.5220/0004859806910699