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, Volume 75, Issue 9, pp 5055–5073 | Cite as

Siamese multi-layer perceptrons for dimensionality reduction and face identification

  • Lilei Zheng
  • Stefan Duffner
  • Khalid Idrissi
  • Christophe Garcia
  • Atilla Baskurt
Article

Abstract

This paper presents a framework using siamese Multi-layer Perceptrons (MLP) for supervised dimensionality reduction and face identification. Compared with the classical MLP that trains on fully labeled data, the siamese MLP learns on side information only, i.e., how similar of data examples are to each other. In this study, we compare it with the classical MLP on the problem of face identification. Experimental results on the Extended Yale B database demonstrate that the siamese MLP training with side information achieves comparable classification performance with the classical MLP training on fully labeled data. Besides, while the classical MLP fixes the dimension of the output space, the siamese MLP allows flexible output dimension, hence we also apply the siamese MLP for visualization of the dimensionality reduction to the 2-d and 3-d spaces.

Keywords

Siamese neural networks Multi-layer perceptrons Metric learning Face identification Dimensionality reduction 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Lilei Zheng
    • 1
  • Stefan Duffner
    • 1
  • Khalid Idrissi
    • 1
  • Christophe Garcia
    • 1
  • Atilla Baskurt
    • 1
  1. 1.University of Lyon, LIRIS - CNRS, National Institute of Applied Sciences (INSA)LyonFrance

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