Apparent Age Estimation Using Ensemble of Deep Learning Models
This addresses a computer vision problem for age estimation applications, but it is incremental as it builds on existing methods like VGG-16 and ensemble techniques.
The paper tackles apparent age estimation from face images, which involves multiple age labels per image due to varying human perceptions, by grouping images into age ranges and training an ensemble of deep learning models, achieving an error of 0.3668 on the ChaLearn LAP 2016 test set.
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].