Abstract
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.
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Nguyen, H.V., Bai, L. (2010). Face Verification Using Indirect Neighbourhood Components Analysis. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6454. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17274-8_62
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DOI: https://doi.org/10.1007/978-3-642-17274-8_62
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