Gender classification

Keywords: Neural, Networks

Background

With the introduction of neural networks in the datasets, there are different types of issues that have been solved in the real-time world. Prediction of gender can be done by the inclusion of different deep learning techniques. There are high chances of predictions as ANN has the capacity to analyze the data better when compared with the other machine learning techniques. Gender classification is one of the major problems in the current scenarios as it has its applications in different fields. Prediction of gender can help in different medical fields, and they are mostly useful during elections, and medical fields. ANN is one of the strong deep-learning architectures that is being used in solving critical and complex tasks as it can have in-depth interaction with the dataset. ANN uses different kinds of layered architecture to gain important information from the datasets. Some of the useful information that can be helpful in predicting gender is forehead_width_cm, long_hair, lips_thin, and distance_nose_to_lip_long. Overall after the application of the ANN architecture the author has obtained an accuracy score of 96%.

Aim & Objectives

Aim
To Classify the Gender using DL Techniques
Objective
Utilize an ANN model to accurately classify gender based on facial or biometric features, aiding in identity verification.
Develop an ANN system that analyzes visual cues to distinguish between male and female individuals.
Evaluate the ANN's gender classification accuracy, contributing to diverse applications in security, marketing, and more.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

— Preview truncated. The complete document (full chapters, references, diagrams, and appendices) is shared with clients as part of project delivery. —