Age and Gender Estimation via Deep Dictionary Learning Regression
Vanika Singhal, Angshul Majumdar · 2019
The paper addresses the problem of estimating age and gender from frontal photos. Most prior studies on deep learning based estimation formulate it as a convolutional neural network based classification problem. For gender it is a two class problem; for age, several age brackets are created to form classes. In this work we formulate it as a regression problem. This is a natural way to handle both gender and age. Gender can be represented as a single variable possible of taking binary values (male or female) whereas age can be represented by a single variable taking non-negative real values. We formulate regression on the newly proposed deep dictionary learning framework. Prior work on this topic, is on unsupervised representation learning; in this work we in-built regression into the deep dictionary learning framework making the formulation supervised. Testing has been done on several state-of-the-art datasets - Adience, MORPH, ChaLearn LAP, LFWA and CelebA. Our method yields age and gender estimation results better than the state-of-the-art.