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