Logistic similarity metric learning for face verification

Lilei Zheng; Khalid Idrissi; Christophe Garcia; Stefan Duffner; Atilla Baskurt

ICASSP 2015accepted0 citations

Logistic similarity metric learning for face verification

Lilei Zheng, Khalid Idrissi, Christophe Garcia, Stefan Duffner, Atilla Baskurt

Abstract

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.

BibTeX

@inproceedings{icassp2015_logisticsimilari,
  title = {Logistic similarity metric learning for face verification},
  author = {Lilei Zheng and Khalid Idrissi and Christophe Garcia and Stefan Duffner and Atilla Baskurt},
  booktitle = {ICASSP 2015},
  year = {2015}
}