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Communication Dans Un Congrès Année : 2013

Learning a bag of features based nonlinear metric for facial similarity

Grégoire Lefebvre

Résumé

This article presents a new method aiming at automatically learning a visual similarity between two images from a class model. This kind of problem is present in many research domains such as object tracking, image classification , signing identification, etc. We propose a new method for facial recognition with a system based on non-linear projection and metric learning. To achieve this objective, we feed a " Bag of Features " representation of the face images into a specific neural network that learns a mapping to a more compact and discriminant representation. This learning process aims at non-linearly projecting the facial features into a reduced space where two images belonging to the same category (i.e. a person) are " close " according to a given similarity metric and " distant " otherwise. The proposed method gives very promising results for face identification in adverse conditions like expression, illumination and facial pose variations. Experimental results give 97% correct recognition rate on the CMU PIE database containing 68 individuals, under vary variable pose and illumination conditions.
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Dates et versions

hal-01218768 , version 1 (22-10-2015)

Identifiants

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Grégoire Lefebvre, Christophe Garcia. Learning a bag of features based nonlinear metric for facial similarity. Advanced Video and Signal Based Surveillance (AVSS), 2013 10th IEEE International Conference on, Aug 2013, Krakow, Poland. ⟨10.1109/AVSS.2013.6636646⟩. ⟨hal-01218768⟩
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