(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Age and gender classification has become applicable to an extending measure of applications, particularly resulting to the ascent of social platforms and social media. Regardless, execution of existing strategies on real-world images is still fundamentally missing, especially when considered the immense bounced 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 Convolutiona l Neural Network (CNN) and Extreme Learning Machine (ELM). CNN is used to extract the features from the input images while ELM classifies the intermediate results. We experiment our architecture on the recent Adience 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.