An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time, Fully Automatic Upper Facial Feature Tracking
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Automatic Eye Detection and Its Validation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
An Improved SNoW Based Classification Technique for Head-pose Estimation and Face Detection
AIPR '05 Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop
Journal of Cognitive Neuroscience
A recursive thresholding technique for image segmentation
IEEE Transactions on Image Processing
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Novel techniques for accurate location of the eyes and nose of a person in a complex-lighting environment are presented in this paper. An adaptive progressive thresholding technique is applied to spot the darkest regions representing the eyes in a face. The nose region is located by performing cumulative histogram-based thresholding of the gradient image formed below the eye region. A feature-specific modular Principal Component Analysis (PCA) approach on face images is performed with the identified features for face recognition. Principal components are extracted from non-overlapping modules of the image and are concatenated to make a single signature vector to represent the face in a particular viewing angle. Additional principal components are extracted from the key facial features and are added as an extension to the signature vector. The feature-specific modular PCA approach is capable of recognising faces in varying illumination conditions and facial expressions, as the modular components represent the local information of the facial regions.