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Face Recognition using Convolutional Neural Network and Simple Logistic Classifier

Face Recognition using Convolutional Neural Network and Simple Logistic Classifier. Hurieh Khalajzadeh Mohammad Mansouri Mohammad Teshnehlab. Table of Contents. Convolutional Neural Networks Proposed CNN structure for face recognition Logistic Classifier

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Face Recognition using Convolutional Neural Network and Simple Logistic Classifier

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  1. Face Recognition using Convolutional Neural Network and Simple Logistic Classifier HuriehKhalajzadeh Mohammad Mansouri Mohammad Teshnehlab

  2. Table of Contents • Convolutional Neural Networks • Proposed CNN structure for face recognition • Logistic Classifier • Result of CNN with winner takes all mechanism • Comparison of using different algorithms for classifying • Results of proposed method • Conclusion

  3. Convolutional Neural Networks • Introduced by YannLeCun and YoshuaBengio in 1995 • Feed-forward networks with the ability of extracting topological properties from the input image • Invariance to distortions and simple geometric transformations like translation, scaling, rotation and squeezing • Alternate between convolution layers and subsampling layers

  4. LeNet5 Architecture

  5. CNN structure used for feature extraction

  6. Interconnection of first subsampling layer with the second convolutional layer

  7. Learning Rate

  8. Yale face database 64×64 [-1, 1]

  9. logistic function

  10. Recognition accuracy, training time and number of parameters

  11. Comparison of different algorithms

  12. X. Shu et al. / Pattern Recognition 45 (2012) 1892-1898

  13. Classification accuracy

  14. Classification time

  15. Conclusion • Convolutional neural networks and simple logistic regression method are investigated with results on Yale face dataset • Method benefit from all CNN advantages such as feature extracting and robustness to distortions • Simple logistic regression which is a discriminative classifier is more efficient when the normality assumptions are satisfied. • Results show the highest classification accuracy and lowest classification time in compare with other machine learning algorithms

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