One-to-many neural network mapping techniques for face image synthesis

Authors:
C. Jayne;A. Lanitis;C. Christodoulou
Affiliations:
Department of Computing, Coventry University, Priory Street, Coventry CV1 5FB, UK;Department of Multimedia and Graphic Arts, Faculty of Communication and Applied Arts, Cyprus University of Technology, 31 Archbishop Kyprianos Street, P.O. Box 50329, 3603 Lemesos, Cyprus;Department of Computer Science, University of Cyprus, 75 Kallipoleos Avenue, P.O. Box 20537, 1678 Nicosia, Cyprus
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

This paper investigates the performance of neural network-based techniques applied to the problem of defining the relationship between a particular type of variation in face images and the multivariate data distributions of these images. In this respect the problem of defining a mapping associating a quantified facial attribute and the overall typical facial appearance is addressed. In particular the applicability of formulating a mapping function using neural network-based methods like Multilayer Perceptrons (MLPs), Radial Basis Functions (RBFs), Mixture Density Networks (MDNs) and a latent variable method, the General Topographic Mapping (GTM) is investigated. Quantitative and visual results obtained during the experimental investigation, suggest that for one-to-many problems, where the entire variance of the distribution is not required, the RBFs are the best options when compared to MLPs, MDNs and GTM. The proposed techniques can be applied to applications involving face image synthesis and other applications that require one-to-many mapping transformations.