ICRL: Using landmark ratios with cascade model for an accurate age estimation system using deep neural networks

Abstract

Over the past decade, the computer vision community has given increased attention to the development of age estimation systems. Several approaches to more accurate and robust facial age estimation have been introduced. Apparent age datasets are typically collected from uncontrolled environments, leading to a number of challenges. In this paper, a cascade model system, which we called the ‘Integrated Classification and Regression with Landmark Ratios (ICRL), is introduced. Our system uses a classification model in order to learn the age label distribution, then uses this knowledge as an auxiliary input to a regression model. ICRL is based on context facial information and label distribution analysis. Facial context information is introduced through the extraction of precise facial landmark ratios. Extracted landmark ratios allow the system to distinguish each age label. The ICRL system uses a classification model to train the CNN network to learn the in-between relation of age labels. ICRL sufficiently models the aging process in the form of ordered and continuous imagery. The ICRL system minimizes the number of parameters needed as well as overall computational costs whilst maintaining robust and accurate results. Despite its simplicity, our system has outperformed other state-of-the-art approaches when applied onto the MORPH II, CLAP2015, AFAD and UTKFace datasets. ICRL achieved an overall superior predictive performance, reaching 99.67% with MORPH II, 99.51% with AFAD, 96.52 with CLAP2015, and 96.28% with UTKFace.