Example Based Learning for View-Based Human Face Detection
Example Based Learning for View-Based Human Face Detection
Receptive Field Structures for Recognition
Neural Computation
A fast eye location method using ordinal features
Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Region-based representations for face recognition
ACM Transactions on Applied Perception (TAP)
Real-Time Face Verification for Mobile Platforms
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Structured ordinal features for appearance-based object representation
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Online feature selection using mutual information for real-time multi-view object tracking
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Face recognition using ordinal features
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Iris recognition based on non-local comparisons
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Biologically motivated visual selective attention for face localization
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
| Hi-index | 0.00 |
The success of any object recognition system, whether biological or artificial, lies in using appropriate representation schemes. The schemes should efficiently encode object concepts while being tolerant to appearance variations induced by changes in viewing geometry and illumination. Here, we present a biologically plausible representation scheme wherein objects are encoded as sets of qualitative image measurements. Our emphasis on the use of qualitative measurements renders the representations stable in the presence of sensor noise and significant changes in object appearance. We develop our ideas in the context of the task of face-detection under varying illumination. Our approach uses qualitative photometric measurements to construct a face signature ('ratio-template') that is largely invariant to illumination changes.