Deep convolutional neural networks-based age and gender classification with facial images

Abstract

In this paper, we build an age and gender classification system including two networks to classify age and gender based on GoogLeNet with the help of Caffe deep learning framework. It outputs gender and age groups of the facial images captured from the camera. We use Adience dataset to train GoogLeNet. Asynchronous Stochastic Gradient Descent based on multi-GPUs is used to optimize training process. We intend to use the trained network to build a classification system in real world to show the practicability. For instance, it can apply to a targeted delivery in bus stop or department store. The results indicate that the accuracy of the classification network can be improved by pre-training. In addition, the multi-GPUs training platform can improve the training speed during the recognition. Overall system reaches speed of 8fps with a high accuracy to classify age and gender.