Multimodal Age-Group Recognition for Opinion Video Logs using Ensemble of Neural Networks

Abstract

With the wide spread usage of smartphones and social media platforms, video logging is gaining an increasing popularity, especially after the advent of YouTube in 2005 with hundred millions of views per day. It has attracted interest of many people with immense emerging applications, e.g. filmmakers, journalists, product advertisers, entrepreneurs, educators and many others. Nowadays, people express and share their opinions online on various daily issues using different forms of content including texts, audios, images and videos. This study presents a multimodal approach for recognizing the speaker's age group from social media videos. Several structures of Artificial Neural Networks (ANNs) are presented and evaluated using standalone modalities. Moreover, a two-stage ensemble network is proposed to combine multiple modalities. In addition, a corpus of videos has been collected and prepared for multimodal age-group recognition with focus on Arabic language speakers. The experimental results demonstrated that combining different modalities can mitigate the limitations of unimodal recognition systems and lead to significant improvements in the results.

A Comprehensive Literature Review on Children’s Databases for Machine Learning Applications

“…This task can be considered as a regression task in which the system can detect the age of child in years. It can be, also, considered as a classification problem to detect the agegroup of a subject as child, adult, senior adult, elder [106] or as infant, baby, toddler, preschool, etc. For example, Safavi et al [107] utilized OGI kids database [32], which mainly developed for speech recognition, to develop a technique for identifying children age-groups from their speech.…”