Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graph Based Approach for Naming Faces in News Photos
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
On the Use of SIFT Features for Face Authentication
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Bag-of-Words Vector Quantization Based Face Identification
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02
Face recognition using SIFT features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Privacy preserving picture sharing: enforcing usage control in distributed on-line social networks
Proceedings of the Fifth Workshop on Social Network Systems
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In this paper we propose a new face recognition approach based on DAISY, a dense computed SIFT-like descriptor. Our algorithm is designed to be fast for dense computation, and useful for re-identification as it is able to distinguish pairs of images as belonging to the same subject or not. The descriptors are computed densely and matched with a new strategy that represents an efficient trade off between accuracy and computational load; afterwards a Support Vector Machine is used to classify the output of the matching to recognize if the pair of images belongs to the same person. An analysis of performance will be conducted on two different databases in order to compare our results with the already existing ones. We show that better performance than SIFT techniques can be achieved using our algorithm.