1 / 15

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

urania
Télécharger la présentation

Face Recognition using Convolutional Neural Network and Simple Logistic Classifier

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related