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Triangular Similarity Metric Learning for Face Verification

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Abstract

We propose an efficient linear similarity metric learning method for face verification called Triangular Similarity Metric Learning (TSML). Compared with the relevant stateof-the-art work, this method improves the efficiency of learning the cosine similarity while keeping effectiveness. Concretely, we present a geometrical interpretation based on the triangle inequality for developing a cost function and its efficient gradient function. We formulate the cost function as an optimization problem and solve it with the advanced L-BFGS optimization algorithm. We perform extensive experiments on the LFW data set using four descriptors: LBP, OCLBP, SIFT and Gabor wavelets. Moreover, for the optimization problem, we test two kinds of initialization: the identity matrix and the WCCN matrix. Experimental results demonstrate that both of the two initializations are efficient and that our method achieves the state-of-the-art performance on the problem of face verification.
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Dates and versions

hal-01158908 , version 1 (02-06-2015)

Identifiers

  • HAL Id : hal-01158908 , version 1

Cite

Lilei Zheng, Khalid Idrissi, Christophe Garcia, Stefan Duffner, Atilla Baskurt. Triangular Similarity Metric Learning for Face Verification. 11th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2015), May 2015, Ljubljana, Slovenia. ⟨hal-01158908⟩
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