Automatic Age and Gender Estimation using Deep Learning and Extreme Learning Machine

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Anto A Micheala, R Shankar


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.

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