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Logistic Similarity Metric Learning for Face Verification


This paper presents a new method for similarity metric learning , called Logistic Similarity Metric Learning (LSML), where the cost is formulated as the logistic loss function, which gives a probability estimation of a pair of faces being similar. Especially, we propose to shift the similarity decision boundary gaining significant performance improvement. We test the proposed method on the face verification problem using four single face descriptors: LBP, OCLBP, SIFT and Gabor wavelets. Extensive experimental results on the LFW-a data set demonstrate that the proposed method achieves competitive state-of-the-art performance on the problem of face verification.
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hal-01158949 , version 1 (02-06-2015)


  • HAL Id : hal-01158949 , version 1


Lilei Zheng, Khalid Idrissi, Christophe Garcia, Stefan Duffner, Atilla Baskurt. Logistic Similarity Metric Learning for Face Verification. 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015, Apr 2015, Brisbane, Australia. ⟨hal-01158949⟩
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