What is the goal of sensory coding?
Neural Computation
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Qualitative Representations for Recognition
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Role of Featural and Configural Information in Familiar and Unfamiliar Face Recognition
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Receptive Field Structures for Recognition
Neural Computation
The CSU face identification evaluation system: its purpose, features, and structure
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Robust human authentication using appearance and holistic anthropometric features
Pattern Recognition Letters
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Face recognition is one of the most important applied aspects of visual perception. To create an automated face-recognition system, the fundamental challenge is that of finding useful features. In this paper, we suggest a new class of image features that may be a useful addition to the set of representational tools for face-recognition tasks. Our proposal is motivated by the observation that rather than relying exclusively on traditional edge-based image representations, it may be useful to also employ region-based strategies that can compare noncontiguous image regions. The spatial homogeneity within regions allows for enhanced tolerance to geometric distortions and greater freedom in the choice of sample points. We first show that under certain circumstances, comparisons between spatially disjoint image regions are, on average, more valuable for recognition than features that measure local contrast. Second, we learn “optimal” sets of region comparisons for recognizing faces across varying pose and illumination. We propose a representational primitive---the dissociated dipole---that permits an integration of edge-based and region-based representations. This primitive is then evaluated using the FERET database of face images and then compared to established local and global algorithms.