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Since the convolutional neural network (CNN) is be- lieved to find rightfeatures for a given problem, the study of hand-crafted features is somewhatneglected these days. In this paper, we show that finding an appropriatefeature for the given problem may be still important as they can en- hance theperformance of CNN-based algorithms. Specif- ically, we show that feeding anappropriate feature to the CNN enhances its performance in some face relatedworks such as age/gender estimation, face detection and emotion recognition. Weuse Gabor filter bank responses for these tasks, feeding them to the CNN alongwith the input image. The stack of image and Gabor responses can be fed to theCNN as a tensor input, or as a fused image which is a weighted sum of image andGabor responses. The Gabor filter parameters can also be tuned depending on thegiven problem, for increasing the performance. From the extensive experiments,it is shown that the proposed methods provide better performance than theconventional CNN-based methods that use only the input images.

Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks