Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
View-Based Active Appearance Models
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Active Appearance Models Revisited
International Journal of Computer Vision
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
An image preprocessing algorithm for illumination invariant face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
| Hi-index | 0.00 |
In this paper we address the challenge of performing face recognition of a probe set of non-frontal images by performing automatic pose correction using Active Appearance Models (AAMs) and matching against a set of enrollment gallery of frontal images. Active Appearance Models are used as a way to register and fitting the model to extract 79 facial fiducial points which are then used to partition the face into a wire-mesh of triangular polygons which are used to warp the facial image to a frontal facial mesh pose. We extend to use View-Based Active Appearance Models (VBAAMs) which are able to represent a preset number of poses better than synthesizing a single AAM to handle all possible pose variations. We demonstrate our approach is able to achieve high performance results on the new larger CMU Multi-PIE dataset using 249 different people with 15 different pose angles and 20 different illumination variations under 2 different expressions (total of 149400 images). We show that our proposed pose correction approach can improve the recognition performance of many baseline algorithms such PCA, LDA, Kernel Discriminant Analysis (KDA) on the CMU Multi-PIE dataset.