Advances in Face Image Analysis: Theory and Applications by Неизв.

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Advances in neural information processing systems. 2012. ; p. -1114. R.. 0580.. , Schmidhuber J.. 0183.. , Schmidhuber J.. , . Multi-column deep neural networks for image classification.. IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2012. p. -3646. , Fergus R.. , Computer Vision – ECCV 2014. arXiv:1311. 2901.. , Friensen E.. , . Facial Action Coding System(FACS): Manual. Palo Alto: Consulting Psychologists Press; 1978. , Tian Y.. , . Comprehensive database for facial expression analysis..

For example, a target could be localized by means of classifying the sliding windows [22]. The reason why CNNs also take an essential part in solving the localization problem of ILSVRC is that CNNs have both the abilities of localization and classification which are discussed here. Table 5 Results of type II feature generalization (generalizing to different classifier & data). 87% 5 Experiments on Feature Generalization Nowadays the research on feature generalization of CNNs is a hot issue. The activations of hidden layers can be considered as a kind of feature representation of the input data.

The exact position can be determined by experiments. As shown in Table 6, the performance is improved by combining features from different layers. But 6 more times of the SVM training time was paid as the price. The advantages of feature generalization are concluded as follows: Firstly, SVM classifiers are not so helpful for us to classify high dimensional image data. However, the performance will be enhanced when a valid feature extractor is employed. 6s, 2s and 5s respectively, which is far less than the time for retraining an entire CNN.

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