Introduction to non-linear optimization
Introduction to non-linear optimization
Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training products of experts by minimizing contrastive divergence
Neural Computation
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Face Recognition Based on the Appearance of Local Regions
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast learning algorithm for deep belief nets
Neural Computation
Patch-Based Gabor Fisher Classifier for Face Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical evaluation of deep architectures on problems with many factors of variation
Proceedings of the 24th international conference on Machine learning
Journal of Cognitive Neuroscience
Advanced Pattern Recognition Technologies with Applications to Biometrics
Advanced Pattern Recognition Technologies with Applications to Biometrics
Reconstruction and recognition of face and digit images using autoencoders
Neural Computing and Applications
Decision Fusion for Patch-Based Face Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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This paper presents research findings on the use of Deep Belief Networks (DBNs) for face recognition. Experiments were conducted to compare the performance of a DBN trained using whole images with that of several DBN trained using image blocks. Image blocks are obtained when the face images are divided into smaller blocks. The objective of using image blocks is to improve the performance of the present DBN to visual variations. To test this hypothesis, the proposed block-based DBN was tested on different databases, which contain a variety of visual variations. Simulation results on these databases show that the proposed block-based DBN is effective against lighting variation. The proposed approach is also compared with other illumination invariant methods and was found to demonstrate higher recognition accuracies.