An ensemble metric learning scheme for face recognition

Abstract

The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods and other techniques that rely on distances or similarities. This paper proposes an ensemble learning technique which combines the efforts of multiple metric learning algorithms like Large Margin Nearest Neighbours (LMNN), Local Fisher Discriminant Analysis (LFDA), Logistic Discriminant Metric Learning (LDML) and a few others to solve the problem of face recognition. In the ensemble learning technique, we propose and study 4 kinds of weighting schemes, namely (1) hard voting, (2) equally weighted soft voting, (3) adaptive soft weighting, and (4) decision tree/neural network based soft voting. In this paper, we present our results compared to Support Vector Machines (SVMs). Experiments show that our proposed method attains state-of-the-art results on the challenging Labeled Faces in the Wild (LFW) dataset [1].