Psychophysically inspired bayesian occlusion model to recognize occluded faces

Authors:
Ibrahim Venkat;Ahamad Tajudin Khader;K. G. Subramanian;Philippe De Wilde
Affiliations:
School of Computer Sciences, Universiti Sains Malaysia, Malaysia;School of Computer Sciences, Universiti Sains Malaysia, Malaysia;School of Computer Sciences, Universiti Sains Malaysia, Malaysia;School of Math. & Comp. Sciences, Heriot-Watt University, Edinburgh, UK
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Year:
2011

Citing 6
Cited 1

Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective Representation Using ICA for Face Recognition Robust to Local Distortion and Partial Occlusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity in Perception: A Window to Brain Organization

Journal of Cognitive Neuroscience
Bayesian Networks and Decision Graphs

Bayesian Networks and Decision Graphs
Recognizing faces using Adaptively Weighted Sub-Gabor Array from a single sample image per enrolled subject

Image and Vision Computing
Face recognition by independent component analysis

IEEE Transactions on Neural Networks

Recognizing occluded faces by exploiting psychophysically inspired similarity maps

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Face recognition systems robust to major occlusions have wide applications ranging from consumer products with biometric features to surveillance and law enforcement applications. In unconstrained scenarios, faces are often subject to occlusions, apart from common variations such as pose, illumination, scale, orientation and so on. In this paper we propose a novel Bayesian oriented occlusion model inspired by psychophysical mechanisms to recognize faces prone to occlusions amidst other common variations. We have discovered and modeled similarity maps that exist in facial domains by means of Bayesian Networks. The proposed model is capable of efficiently learning and exploiting these maps from the facial domain. Hence it can tackle the occlusion uncertainty reasonably well. Improved recognition rates over state of the art techniques have been observed.