To Analyze and Detect Gender by Using Convolution Neural Networks


Authors : B. Nagaraju; A. Venkata Reddy; T. Sri Manjunadha; M. Mohan Kumar

Volume/Issue : Volume 8 - 2023, Issue 4 - April

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://bit.ly/3pcFvWi

DOI : https://doi.org/10.5281/zenodo.7902020

The project titled “GENDER CLASSIFICATION” predicts gender based on human bodies or behavior. In this paper, we have worked on a technique for classification of gender using python algorithm. Human classification and identification have been used for a very long period in many different fields. areas such as government identification cards, verification processes, etc. For the purpose of identifying people, we have already created methods such as the retinal scan, iris scan, fingerprint, and other complex systems like DNA fingerprinting. Despite the fact that these already developed methods are effective, the hardware, software, and human competency requirements are far too onerous for a number of straightforward tasks that may or may not call for professional efficiency. Technique reported in this paper is simple and easy for gender classification which can be performed using only a webcam and a decent computer system. Automatic gender classification has become relevant to an increasing number of applications, particularly since the rise of social platforms and social media. Even Nevertheless, compared to the enormous performance improvements recently reported for the closely related task of facial recognition, the performance of present approaches on real-world photographs still falls far short. In this paper, we demonstrate that performance on these tasks can be significantly improved by learning representations using deep convolutional neural networks (CNN). In order to do this, we suggest a straightforward convolutional net architecture that may be applied even with a finite supply of training data. We evaluate our method on the recent Audience benchmark for gender estimation and show it to dramatically outperform current state-of-theart methods.

Keywords : Convolution Neural Networks, Machine Learning, Python, Support Vector Machine.

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