Skip to Main content Skip to Navigation
Journal articles

Siamese Multi-layer Perceptrons for Dimensionality Reduction and Face Identification

Lilei Zheng 1 Stefan Duffner 1 Khalid Idrissi 1 Christophe Garcia 1 Atilla Baskurt 1 
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : This paper presents a framework using siamese Multi-layer Percep-trons (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.
Document type :
Journal articles
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download
Contributor : Lilei Zheng Connect in order to contact the contributor
Submitted on : Friday, July 31, 2015 - 3:02:43 PM
Last modification on : Thursday, September 9, 2021 - 2:36:02 PM
Long-term archiving on: : Sunday, November 1, 2015 - 10:21:46 AM


Files produced by the author(s)



Lilei Zheng, Stefan Duffner, Khalid Idrissi, Christophe Garcia, Atilla Baskurt. Siamese Multi-layer Perceptrons for Dimensionality Reduction and Face Identification. Multimedia Tools and Applications, Springer Verlag, 2015, pp.,. ⟨10.1007/s11042-015-2847-3⟩. ⟨hal-01182273⟩



Record views


Files downloads