Real-time Age, Gender and Emotion Detection using Caffe Models

Abstract

Age and gender classification has become applicable to an extending measure of applications, particularly resulting in the ascent of social platforms and social media. Regardless, execution of existing strategies on real-world images is still fundamentally missing, especially when considering the immense bounce in execution starting late reported for the related task of face acknowledgment. In this paper we exhibit that by learning representations through the use of significant Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM). CNN is used to extract the features from the input images while ELM defines the intermediate results. We experiment our architecture on the recent Audience benchmark for age and gender estimation and demonstrate it to radically outflank current state-of-the-art methods. Experimental results show that our architecture outperforms other studies by exhibiting significant performance improvement in terms of accuracy and efficiency.