Age and Gender Prediction from Facial Features Using Deep Learning

Authors

  • Ankita Mondal B.E. Student, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bangalore, India
  • Sonam Verma B.E. Student, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bangalore, India
  • Sankalp Sanjay B.E. Student, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bangalore, India
  • Sumithra Devi Head of the Department, Department of Information Science and Engineering, Dayanand Sagar Academy of Technology and Management, Bangalore, India

Keywords:

Cascade classifier for facial features extraction, Faster RCNN architecture, Machine Learning (ML), Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN)

Abstract

Automatic age and gender classification has been significant to a rising variety of applications since the rise of social platforms and social media. The performance of existing algorithms on real-world images, on the other hand, is pathetically inadequate, especially when compared to the massive leaps in performance recently reported for the related task of facial recognition. In this paper, we show that learning representations with deep convolutional neural networks (CNN) can result in considerable improvements in performance on certain tasks [1]. We propose a simple convolutional net design that can be used even when learning data is scarce to do it.

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Published

15-07-2022

How to Cite

[1]
A. Mondal, S. Verma, S. Sanjay, and S. Devi, “Age and Gender Prediction from Facial Features Using Deep Learning”, IJRESM, vol. 5, no. 7, pp. 42–43, Jul. 2022.

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Articles