Gender and Age Estimation without Facial Information from Still Images

EasyChair Preprint 4308

Abstract

In this paper, the task of gender and age recognition is per- formed on pedestrian still images, which are usually captured in-the-wild with no near face-frontal information. Moreover, another diculty origi- nates from the underlying class imbalance in real examples, especially for the age estimation problem. The scope of the paper is to examine how dierent loss functions in convolutional neural networks (CNN) perform under the class imbalance problem. For this purpose, as a backbone, we employ the Residual Network (ResNet). On top of that, we attempt to benet from appearance-based attributes, which are inherently present in the available data. We incorporate this knowledge in an autoencoder, which we attach to our baseline CNN for the combined model to jointly learn the features and increase the classication accuracy. Finally, all of our experiments are evaluated on two publicly available datasets.

Keyphrases: Age estimation, Deep Imbalanced Learning, Gender Classication

@booklet{EasyChair:4308,
  author    = {Georgia Chatzitzisi and Michalis Vrigkas and Christophoros Nikou},
  title     = {Gender and Age Estimation without Facial Information from Still Images},
  howpublished = {EasyChair Preprint 4308},
  year      = {EasyChair, 2020}}