 |
Conference papers
Logistic Similarity Metric Learning for Face Verification
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.
Cited literature [26 references]
https://hal.archives-ouvertes.fr/hal-01158949
Contributor : Lilei Zheng <>
Submitted on : Tuesday, June 2, 2015 - 11:16:26 AM
Last modification on : Wednesday, July 8, 2020 - 12:43:36 PM
Long-term archiving on: : Tuesday, September 15, 2015 - 9:26:32 AM
Contributor : Lilei Zheng <>
Submitted on : Tuesday, June 2, 2015 - 11:16:26 AM
Last modification on : Wednesday, July 8, 2020 - 12:43:36 PM
Long-term archiving on: : Tuesday, September 15, 2015 - 9:26:32 AM
File
Liris-7045.pdf
Files produced by the author(s)