AI-based Gender Identification using Facial Features
The growing concern for ethics and security has led to a surge in interest surrounding automated gender identification in recent times. This project presents a robust AI-based Gender Identification system employing the power of Convolutional Neural Networks (CNNs) within the MATLAB environment. The proposed system boasts an impressive accuracy rate of 98.75%, demonstrating its efficacy in real-world applications.
The project commences with the collection of a comprehensive dataset of facial images, serving as the foundational resource for training and evaluation. Subsequently, a state-of-the-art computer vision face detection model is introduced, specifically designed for accurate face region detection within testing images. This step is critical in isolating the relevant facial features for subsequent analysis.
To enhance the quality and consistency of the data, preprocessing is performed as a crucial intermediate step. In this phase, the images are resized to a standardized format, ensuring that the deep learning model receives consistent input, thereby optimizing its performance.
The core of the project lies in the utilization of the AlexNet Convolutional Neural Network (CNN) architecture. This deep learning model serves a dual purpose: extracting intricate facial features and accurately classifying gender based on these features. The AlexNet-CNN model, renowned for its ability to learn complex hierarchical representations, excels in this task, contributing to the remarkable accuracy achieved.
In summary, the project encapsulates a comprehensive pipeline for AI-based Gender Identification, spanning from data collection to preprocessing and culminating in the deployment of an advanced deep learning model. The achieved accuracy of 98.75% underscores the system’s effectiveness and its potential for integration into real-world scenarios, including but not limited to security, marketing, and human-computer interaction. This innovative approach underscores the capacity of AI and deep learning to decipher intricate information from facial features, opening new avenues for research and application in the fields of computer vision and artificial intelligence.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : MATLAB
- Tool : MATLABR2023B
B.Venkateswarlu, N.Sunanda, A Yaswanth Mani Kumar, A Naga Siva Charan Reddy ,B.Ram Gopal Hyndav, “AI-based Gender Identification using Facial Features”, 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), IEEE Conference, 2023.