In this paper, we propose a trainable Gabor wavelet (TGW) layer and cascade it with a convolutional neural network (CNN) for the age estimation. Unlike an existing method that uses fixed (hand-tuned) Gabor filters at the head of a CNN, we use Gabor wavelets that can be adapted for the given input as well as for the targeting task. This is enabled by (a) estimating hyperparameters of Gabor wavelets from the input and (b) using a 1 × 1 convolution layer for the selection of orientation parameter. The proposed TGW layers are trained with the standard gradient-descent method and can be easily incorporated with conventional CNNs in an end-to-end training manner. We conduct experiments on the Adience dataset and show that the proposed network outperforms the baseline CNN without TGW layers and efficiently used trainable parameters than ordinary CNN based methods.
This work was supported in part by the SNU-Hojeon Garment Smart Factory Research Center funded by Hojeon Ltd., and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 1711075689, Decentralised cloud technologies for edge/IoT integration in support of AI applications)
