TY - GEN
T1 - Weighted discriminant analysis and kernel ridge regression metric learning for face verification
AU - Chong, Siew Chin
AU - Teoh, Andrew Beng Jin
AU - Ong, Thian Song
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84992553675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992553675&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46672-9_45
DO - 10.1007/978-3-319-46672-9_45
M3 - Conference contribution
AN - SCOPUS:84992553675
SN - 9783319466712
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 401
EP - 410
BT - Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
A2 - Ozawa, Seiichi
A2 - Ikeda, Kazushi
A2 - Liu, Derong
A2 - Hirose, Akira
A2 - Doya, Kenji
A2 - Lee, Minho
PB - Springer Verlag
T2 - 23rd International Conference on Neural Information Processing, ICONIP 2016
Y2 - 16 October 2016 through 21 October 2016
ER -