Adaptive weighted local textural features for illumination, expression and occlusion invariant face recognition


Chen Cui

Date of Award


Degree Name

M.S. in Electrical Engineering


Department of Electrical and Computer Engineering


Advisor: Vijayan K. Asari


Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. Several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition is still remaining as a difficult issue that needs to be resolved. In this thesis, we propose a novel approach to tackle some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern description of all the local regions in the face, and the dimensionality of the texture image is reduced by principal component analysis performed on each local face region independently. The face feature vector is obtained by concatenating the reduced dimensional weight set of each module (sub-region) of the face image. The weightage of each sub-region is determined by employing the local variance estimate of the respective region which represents the significance of the region. Experiments conducted on various popular face databases show promising performance of the proposed algorithm in varying lighting, expression, and partial occlusion conditions. Research work is progressing to investigate the effectiveness of the proposed face recognition method on pose varying conditions as well. It is envisaged that a multilane approach of trained frameworks at different pose bins and an appropriate voting strategy would lead to a good recognition rate in such situation.


Human face recognition (Computer science), Principal components analysis, Optical pattern recognition, Electrical engineering; enhanced local binary pattern; weighted modular principal component analysis; face recognition; feature extraction

Rights Statement

Copyright 2013, author