Pose-invariant face recognition using deformation analysis

Authors:
Taeg-Keun Whangbo;Jae-Young Choi;Murlikrishna Viswanathan;Nak-Bin Kim;Young-Gyu Yang
Affiliations:
Dept. of Computer Science, Kyungwon University;Dept. of Computer Science, University of California, Los Angeles;Dept. of Computer Science, Kyungwon University;Dept. of Computer Science, Kyungwon University;Dept. of Computer Science, Kyungwon University
Venue:
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Year:
2005

Citing 9
Cited 0

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
Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for the Automatic Detection of Human Faces in Color Images

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
PCA versus LDA

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
Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for the Automatic Detection of Human Faces in Color Images

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Over the last decade or so, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. In addition, recognition of faces under varied poses has been a challenging area of research due to the complexity of pose dispersion in feature space. This paper presents a novel and robust pose-invariant face recognition method. In this approach, first, the facial region is detected using the TSL color model. The direction of face or pose is estimated using facial features and 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.