Title: Face recognition using multi-scale differential invariants in statistical manifold framework

Authors: Jian Zou, Chuan-Cai Liu, Yue Zhang, Gui-Fu Lu

Addresses: School of Computer Science and Technology, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, China; School of Mathematics and Physics, Anhui Polytechnic University, East Zheshan Road 8, WuHu, China. ' School of Computer Science and Technology, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, China. ' School of Computer Science and Technology, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, China; School of Mathematics and Physics, Anhui Polytechnic University, East Zheshan Road 8, WuHu, China. ' School of Computer Science and Technology, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, China; School of Mathematics and Physics, Anhui Polytechnic University, East Zheshan Road 8, WuHu, China

Abstract: The local image structure can be robustly represented by multi-scale Gaussian derivatives (GDs) or the derived differential features. However, the high-dimensional nature of concatenated global features makes it hard to be applied directly. To utilise multi-scale Gaussian derivative-based differential invariants (MGDDI) up to order two for face recognition, a novel method of matching probabilistic generating model of MGDDI is developed in statistical manifold framework. It takes MGDDI of an image as multi-channel feature sets in which each one is univariate consisting of fixed dimensional components of local |jets|. Under specific partitions on feature spaces, each channel feature set is modelled as a realisation of a marginal multinomial distribution, and corresponding normalised histogram can be identified with estimated model parameters. With the Fisher geometry on multinomial manifold, a similarity measure is proposed for matching marginal model sets. The effectiveness of proposed method is demonstrated by the promising experimental results on ORL and FERET face database.

Keywords: Gaussian derivatives; differential invariants; statistical manifold; face recognition; similarity measures; image structure.

DOI: 10.1504/IJDMMM.2011.042934

International Journal of Data Mining, Modelling and Management, 2011 Vol.3 No.4, pp.361 - 374

Published online: 08 Oct 2011 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article