Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis: algorithms and applications
Neural Networks
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Face Recognition Using Third-Order Synthetic Neural Networks
Human Face Recognition Using Third-Order Synthetic Neural Networks
Estimation of the Mouth Features Using Deformable Template Matching
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Handbook of Face Recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Face localization via hierarchical CONDENSATION with fisher boosting feature selection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face alignment using statistical models and wavelet features
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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This paper introduces some novel models for all steps of a face recognition system. In the step of face detection, we propose a hybrid model combining AdaBoost and Artificial Neural Network (ABANN) to solve the process efficiently. In the next step, labeled faces detected by ABANN will be aligned by Active Shape Model and Multi Layer Perceptron. In this alignment step, we propose a new 2D local texture model based on Multi Layer Perceptron. The classifier of the model significantly improves the accuracy and the robustness of local searching on faces with expression variation and ambiguous contours. In the feature extraction step, we describe a methodology for improving the efficiency by the association of two methods: geometric feature based method and Independent Component Analysis method. In the face matching step, we apply a model combining many Neural Networks for matching geometric features of human face. The model links many Neural Networks together, so we call it Multi Artificial Neural Network. MIT + CMU database is used for evaluating our proposed methods for face detection and alignment. Finally, the experimental results of all steps on CallTech database show the feasibility of our proposed model.