Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Field Categorization and Edge/Corner Detection from Gradient Covariance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
2D Direct LDA Algorithm for Face Recognition
SERA '06 Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications
Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition
IEICE - Transactions on Information and Systems
Interest Operator versus Gabor filtering for facial imagery classification
Pattern Recognition Letters
Improvement on PCA and 2DPCA algorithms for face recognition
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
IEEE Transactions on Image Processing
Robust and Efficient Image Alignment Based on Relative Gradient Matching
IEEE Transactions on Image Processing
Pattern Recognition Letters
An improvement to matrix-based LDA
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Face recognition by using overlapping block discriminative common vectors
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
| Hi-index | 12.05 |
When the conventional interest operator is used as the feature extraction procedure of face recognition, it has the following two shortcomings: first, though the purpose of the conventional interest operator is to use the intensity variation between neighboring pixels to represent the image, it cannot obtain all variation information between neighboring pixels. Second, under varying lighting conditions two images of the same face usually have different feature extraction results even though the face itself does not have obvious change. In this paper, we propose two new interest operators for face recognition, which are used to calculate the pixel intensity variation information of overlapping blocks produced from the original face image. The following two factors allow the new operators to perform better than the conventional interest operator: the first factor is that by taking the relative rather than absolute variation of the pixel intensity as the feature of an image block, the new operators can obtain robust block features. The second factor is that the scheme to partition an image into overlapping rather than non-overlapping blocks allows the proposed operators to produce more representation information for the face image. Experimental results show that the proposed operators offer significant accuracy improvement over the conventional interest operator.