Digital image processing
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstruction of Partially Damaged Face Images Based on a Morphable Face Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust De-noising by Kernel PCA
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Support Vector Features and the Role of Dimensionality in Face Authentication
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Face recognition from one example view
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Fast features for face authentication under illumination direction changes
Pattern Recognition Letters
Journal of Cognitive Neuroscience
A comparative study of automatic face verification algorithms on the BANCA database
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Robust features for frontal face authentication in difficult image conditions
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Performance evaluation of face recognition algorithms on the asian face database, KFDB
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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Photo image authentication is an interesting and demanding field in the computer vision and image processing community. This research is motivated by its wide range of applications, which include smart card authentication systems, biometric passport systems, etc. In this paper, we propose a method of authenticating corrupted photo images based on noise parameter estimation. The proposed method first generates corrupted images by adjusting the noise parameters in the initial training phase. This set of corrupted images and the noise parameters can be represented by a linear combination of the prototypes of the corrupted images and the noise parameters. In the testing phase, the noise parameters of the corrupted photo image can be estimated with a corrupted image and an original image. Finally, we can make a synthesized photo image from the original photo image using the estimated noise parameters and verify it with the corrupted photo image. The experimental results show that the proposed method can estimate the noise parameters accurately and improve the performance of photo image authentication.