Analysis of Different Neural Network Architectures in Face Recognition System

  • E. V. Sudhanva
  • V. N. Manjunath Aradhya
  • C. Naveena
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)


Face Recognition is considered to be as one of the finest aspects of Computer Vision, also various Feature Extraction and classification techniques including Neural Network Architectures have made it even more interesting. In this paper, an attempt towards developing a model for better feature representation/extraction and cascading it with neural networks classifier is presented. In order to derive better use of face recognition system for faster and better surveillance, analysis is carried out which provides a greater knowledge on the entire process and clarifies on various parameters effecting the system. Most popular Single-Layer Neural Networks such as generalized regression neural network (GRNN) and probabilistic neural network (PNN) are used with different subspace methods to provide a distinguished analysis. The experimental results in this work have revealed that the combination of subspace method with neural networks has increased the robustness and speed of face recognition system. Performance analysis of the proposed model is carried out by conducting the experiments on three benchmarking databases such as ORl, Yale and Feret.


Face recognition Eigen values Eigenfaces PCA KPCA GRNN PNN Recognition rate 


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Copyright information

© Springer India 2016

Authors and Affiliations

  • E. V. Sudhanva
    • 1
  • V. N. Manjunath Aradhya
    • 2
  • C. Naveena
    • 3
  1. 1.Department of CSJain UniversityBangaloreIndia
  2. 2.Department of MCASri Jayachamarajendra College of EngineeringMysoreIndia
  3. 3.Department of CSEHKBK College of EngineeringBangaloreIndia

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