Apparent Age Estimation Using Ensemble of Deep Learning Models

TL;DR

This paper introduces an ensemble of deep learning models for apparent age estimation, leveraging multiple age labels per image and facial landmark alignment to improve accuracy in perceived age prediction.

Contribution

It proposes a novel ensemble approach using aligned CNNs trained on grouped age ranges, addressing multi-label annotation challenges in apparent age estimation.

Findings

Achieved 0.3668 error on ChaLearn LAP 2016 dataset

Utilized facial landmarks for better face alignment

Enhanced age estimation accuracy with ensemble of CNNs

Abstract

In this paper, we address the problem of apparent age estimation. Different from estimating the real age of individuals, in which each face image has a single age label, in this problem, face images have multiple age labels, corresponding to the ages perceived by the annotators, when they look at these images. This provides an intriguing computer vision problem, since in generic image or object classification tasks, it is typical to have a single ground truth label per class. To account for multiple labels per image, instead of using average age of the annotated face image as the class label, we have grouped the face images that are within a specified age range. Using these age groups and their age-shifted groupings, we have trained an ensemble of deep learning models. Before feeding an input face image to a deep learning model, five facial landmark points are detected and used for 2-D alignment. We have employed and fine tuned convolutional neural networks (CNNs) that are based on VGG-16 [24] architecture and pretrained on the IMDB-WIKI dataset [22]. The outputs of these deep learning models are then combined to produce the final estimation. Proposed method achieves 0.3668 error in the final ChaLearn LAP 2016 challenge test set [5].