AI Based Face Recognition & Detection

Abstract

Artificial intelligence (AI) has advanced quickly, resulting in the creation of intelligent algorithms that are very accurate at identifying and detecting human faces. AI-based face identification and detection automatically recognises and validates people from digital photos or video frames by using computer vision and deep learning algorithms. Face detection, which finds one or more faces in a picture, and face recognition, which compares detected faces with stored information to establish identity, are the two primary steps in the process. This technology has become extremely important in several areas, including social media, mobile authentication, security and surveillance, and attendance tracking. Convolutional neural networks (CNNs) and deep learning architectures are examples of contemporary neural network models that have greatly improved the speed and dependability of recognition systems. Despite its benefits, study is still being done on issues including privacy concerns, lighting differences, and changes in face expressions.

Introduction

Artificial intelligence (AI)-based face recognition and detection is a rapidly advancing technology that enables machines to identify and verify human faces from images or videos. It works by detecting facial features such as the eyes, nose, and mouth using computer vision, machine learning, and deep learning—especially Convolutional Neural Networks (CNNs). After detecting a face in an image or video, the system compares it with stored data to recognise the person. This technology is widely used in security systems, smartphones, surveillance, attendance tracking, and access control, offering high accuracy even in challenging conditions like low light or varying facial expressions.

The development process includes five major steps:

1. Data Collection – Gathering diverse face images.

2. Data Preprocessing – Resizing, noise removal, and face cropping using algorithms like Haar Cascade or MTCNN.

3. Feature Extraction – Using CNNs to extract unique facial patterns.

4. Model Training and Recognition – Training the system to match detected faces with stored data.

5. Testing and Deployment – Evaluating performance and deploying the system in real-world applications.

The study highlights that AI-based face recognition significantly improves accuracy and speed compared to traditional methods. Its effectiveness depends heavily on dataset quality and proper preprocessing. Although real-time recognition using OpenCV and deep learning models is reliable, challenges such as lighting variations, facial occlusions (masks, glasses), or angle changes can impact accuracy. Ethical and privacy concerns regarding facial data storage also remain important.

AI-based face recognition has numerous applications, including security and surveillance, mobile authentication, attendance systems, law enforcement, banking verification, healthcare monitoring, retail analytics, and automatic tagging on social media platforms.

Conclusion

The AI-based face recognition and detection system can be enhanced in a number of ways in the future. Larger and more varied datasets can improve recognition accuracy. To handle various lighting conditions, face emotions, and partial occlusions like masks or glasses, the system can be strengthened. For increased security, future research could concentrate on combining face recognition with additional biometric technologies like voice or fingerprint recognition. Optimising algorithms to operate more quickly on mobile and embedded devices can also enhance real-time performance. Secure data management techniques must also be used to address privacy and ethical concerns pertaining to the gathering and storage of facial data. These advancements will contribute to the technology\'s future dependability, security, and broad applicability.

References

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Copyright

Copyright © 2025 Prof. R V Babar, Gaurav Survase, Yuvaraj Bhore, Swaraj Dareker, Vishwajeet Pawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.