Gender Classification Using Deep Learning Techniques

Project Code :TCMAPY960

Objective

The main objective of Gender Classification Using Deep Learning Techniques is to develop an accurate and robust model that can automatically determine the gender of individuals based on their facial or vocal characteristics, enabling applications such as facial recognition systems, voice assistants, and demographic analysis. The aim is to leverage the power of deep learning algorithms to achieve high classification accuracy and improve gender recognition technology.

Abstract

Automatic gender recognition has now pertinent to an extension of its usage in various software and hardware, particularly because of the growth of online social networking websites and social media. However, the performance of already exist system with the physical world face pictures, images are somewhat not excellent, particularly in comparison with the result of task
related to face recognition. Within this paper, we have explored that by doing learn and classification method and with the utilization of Convolutional Neural Networks (CNN) technique, a satisfied growth in performance can be achieved on such gender classification. The tasks that is a reason why we decided to propose an efficient convolutional network architecture which can be used in extreme case when the amount of training data used to learn CNN architecture. We examine our related work on the current unfiltered image of the face for gender recognition and display it to dramatics outplay current advance updated methods.

KEYWORDS: Computer Vision, CNN, Classification, Unfiltered images, Gender recognition.

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