Pose-invariant face recognition using real and virtual views
Pose-invariant face recognition using real and virtual views
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Journal of Cognitive Neuroscience
Hi-index | 0.00 |
Recognition of faces under varied poses has been a challenging area of research due to the complex dispersion of poses in feature space when compared to that of frontal faces. This paper presents a novel and robust pose-invariant face recognition method in order to improvise over existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the direction of face using facial features. The estimated pose vector is decomposed into X-Y-Z axes. Second, the input face is mapped by a deformable template using these vectors and the 3D CANDIDE face model. Finally, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses. Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses.