Thermal IR Face Recognition Using Zernike Moments and Multi Layer Perceptron Neural Network (MLPNN) Classifier

2017 ◽  
pp. 213-222
Author(s):  
Vijayalakshmi G. V. Mahesh ◽  
Alex Noel Joseph Raj ◽  
P. Arulmozhivarman
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Zernike Moments ◽  
Multi Layer Perceptron

FRBF Neural Network base for Face Recognition using Zernike Moments and PCA

2015 ◽  
Vol 125 (2) ◽  
pp. 10-14
Author(s):  
Majid Iranpour ◽  
Mehran Emadi ◽  
Majid Emadi
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Zernike Moments

scholarly journals Applying Artificial Neural Networks for Face Recognition

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Thai Hoang Le
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Geometric Feature ◽  
Multi Layer Perceptron ◽  
Texture Model ◽  
Model Combining ◽  
Artificial Neural

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.

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