ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Pruning Training Sets for Learning of Object Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Face recognition using classification-based linear projections
EURASIP Journal on Advances in Signal Processing
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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Face verification is the task of deciding by analyzing face images, whether a person is who he/she claims to be. This is very challenging due to image variations in lighting, pose, facial expression, and age. The task boils down to computing the distance between two face vectors. As such, appropriate distance metrics are essential for face verification accuracy. In this paper we propose a new method, named the Indirect Neighbourhood Components Analysis (INCA) for learning a distance metric for facial verification. Specifically, INCA is the result of combining ideas from two recently introduced methods: One-shot Similarity learning (OSS) and Neighbourhood Components Analysis (NCA). Our method is tested on the state-of-the-art dataset, the Labeled Faces in the Wild (LFW), and has achieved promising results even in very low dimensions.