Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition from Unfamiliar Views: Subspace Methods and Pose Dependency
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Eigen Light-Fields and Face Recognition Across Pose
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Appearance-Based Face Recognition and Light-Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Creating Invariance to "Nuisance Parameters" in Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Unconstrained Face Recognition (International Series on Biometrics)
Unconstrained Face Recognition (International Series on Biometrics)
A real-time angle- and illumination-aware face recognition system based on artificial neural network
Applied Computational Intelligence and Soft Computing - Special issue on Awareness Science and Engineering
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The need for stricter security, availability of sophisticated algorithms and improved hardware at cheaper price, are the driving forces for increasing popularity of biometric authentication. Machine authentication from face images or fingerprints at the entrance of a building or at bank-teller is getting more and more common. The popularity of using face image features for authentication is due to its ease of use. But its success depends on proper orientation and illumination. Facial features change with the angle of orientation, and even a genuine person would be rejected by the machine due to improper orientation of the face towards the camera. In this work, we proposed a multi-layer perceptron based face identification technique which is robust to orientation of the face image under similar illumination condition. Training data of facial features against orientation angle features is used to train a Multi-Layer Perceptron (MLP). It is then used for interpolation of facial features at different angles. Experiments were conducted with two types offace image identifying features, using PCA and ICA. A good interpolation property could be obtained by the trained MLp, and a zero equal error rate (EER) could be achieved.