@inproceedings{2974c246476d426b85d956ac61a9b84a,
title = "Weighted discriminant analysis and kernel ridge regression metric learning for face verification",
abstract = "A new formulation of metric learning is introduced by assimilating the kernel ridge regression (KRR) and weighted side-information linear discriminant analysis (WSILD) to enjoy the best of both worlds for unconstrained face verification task. To be specific, we formulate a doublet constrained metric learning problem by means of a second degree polynomial kernel function. The said metric learning problem can be solved analytically for Mahalanobis distance metric due to simplistic nature of KRR in which we named KRRML. In addition, the WSILD further enhances the learned Mahalanobis distance metric by leveraging the within-class and between-class scatter matrix of doublets. We evaluate the proposed method with Labeled Faces in the Wild database, a large benchmark dataset targeted for unconstrained face verification. The promising result attests the robustness and feasibility of the proposed method.",
author = "Chong, {Siew Chin} and Teoh, {Andrew Beng Jin} and Ong, {Thian Song}",
year = "2016",
month = jan
day = "1",
doi = "10.1007/978-3-319-46672-9_45",
language = "English",
isbn = "9783319466712",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "401--410",
editor = "Seiichi Ozawa and Kazushi Ikeda and Derong Liu and Akira Hirose and Kenji Doya and Minho Lee",
booktitle = "Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings",
address = "Germany",
note = "23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",
}