Facial Age Estimation based on Discrete Wavelet Transform- Deep Convolutional Neural Networks

Abstract

Facial age estimation based on discrete-wavelet-transform deep convolutional neural networks (DWT-DCNN) is a biometric application based on deep learning. Facial age estimation obtains the range of ages of anonymous by taking his/her face images. Deep learning could be the most promising classification/ regression method for a decade. Almost all deep learning architectures including deep convolutional neural networks (DCNNs) cost millions of parameters and large time on training. With the aid of DWT-DCNN, the training period can be reduced, and the number of parameters could drop in some circumstances. Moreover, the experimental result indicates the relationship between DCNNs and traditional edge detection methods can be discovered. Adience has been chosen as the benchmarks.