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2016

Triplet Probabilistic Embedding for Face Verification and Clustering

Sankaranarayanan, Swami, Alavi, Azadeh, Castillo, Carlos et al.

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Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem.

This paper proposes an approach that couples a deep CNN-based approach with a low-dimensional discriminative embedding learned using triplet probability constraints to solve the unconstrained face verification problem.
Aside from yielding performance improvements, this embedding provides significant advantages in terms of memory and for post-processing operations like subject specific clustering.
Experiments on the challenging IJB-A dataset show that the proposed algorithm performs comparably or better than the state of the art methods in verification and identification metrics, while requiring much less training data and training time.

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