Face Recognition Using Convolutional Neural Network and Simple Logistic Classifier

  • Hurieh Khalajzadeh
  • Mohammad Mansouri
  • Mohammad Teshnehlab
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)


In this paper, a hybrid system is presented in which a convolutional neural network (CNN) and a Logistic regression classifier (LRC) are combined. A CNN is trained to detect and recognize face images, and a LRC is used to classify the features learned by the convolutional network. Applying feature extraction using CNN to normalized data causes the system to cope with faces subject to pose and lighting variations. LRC which is a discriminative classifier is used to classify the extracted features of face images. Discriminant analysis is more efficient when the normality assumptions are satisfied. The comprehensive experiments completed on Yale face database shows improved classification rates in smaller amount of time.


Convolutional Neural Network (CNN) Logistic regression classifier (LRC) Machine learning algorithms Back-propagation and Face Recognition. 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hurieh Khalajzadeh
    • 1
  • Mohammad Mansouri
    • 1
  • Mohammad Teshnehlab
    • 1
  1. 1.K. N. Toosi University of TechnologyTehranIran

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