Deep Cross-Modal Age Estimation

Abstract

Automatic age and gender classification systems can play a vital role in a number of applications including a variety of recommendation systems, face recognition across age progression, and security applications. Current age and gender classifiers, are lacking crucial accuracy and reliability in order to be used in real world applications since most real-time systems have zero fault tolerant. This paper develops an end-to-end, deep architecture aiming to improve the accuracy and reliability of the age estimation task. We design a deep convolutional neural network (CNN) architecture for age estimation that builds upon a gender classification model. The system leverages a gender classifier to improve the accuracy of the age estimator. We investigate several architectures and techniques for the age estimator model with cross-modal learning, including an end-to-end model, using gender embedding of the input image, which leads to an increased accuracy. We evaluated our system on the Adience benchmark, which consists of real-world in-the-wild pictures of faces. We have shown that our system outperforms state-of-the-art age classifiers, such as [1] by 9 %, by training a cross-modal age classifier.