# Multimodal Age and Gender Classification Using Ear and Profile Face Images

## TL;DR

This paper introduces a multimodal deep learning framework combining profile face and ear images to improve age and gender classification accuracy, outperforming existing methods on multiple datasets.

## Contribution

The paper proposes an end-to-end multimodal deep neural network framework utilizing face and ear images with data, feature, and score level fusion, incorporating domain adaptation and center loss for enhanced classification.

## Key findings

- High accuracy in age and gender classification achieved.
- Multimodal approach outperforms single-modality methods.
- Effective use of domain adaptation and center loss.

## Abstract

In this paper, we present multimodal deep neural network frameworks for age and gender classification, which take input a profile face image as well as an ear image. Our main objective is to enhance the accuracy of soft biometric trait extraction from profile face images by additionally utilizing a promising biometric modality: ear appearance. For this purpose, we provided end-to-end multimodal deep learning frameworks. We explored different multimodal strategies by employing data, feature, and score level fusion. To increase representation and discrimination capability of the deep neural networks, we benefited from domain adaptation and employed center loss besides softmax loss. We conducted extensive experiments on the UND-F, UND-J2, and FERET datasets. Experimental results indicated that profile face images contain a rich source of information for age and gender classification. We found that the presented multimodal system achieves very high age and gender classification accuracies. Moreover, we attained superior results compared to the state-of-the-art profile face image or ear image-based age and gender classification methods.