Face detection and filtering have become crucial for managing large-scale image datasets, especially with the increasing volume of digital media. Traditional manual filtering techniques are inefficient, particularly when dealing with thousands of high-resolution images. This paper presents an AI-based approach that identifies and filters a user’s face from 3,000 high-resolution images stored locally. The proposed system integrates deep learning-based face recognition using Python (OpenCV, face_recognition, Pandas) and a React Native mobile application for seamless image management.The system processes large datasets efficiently and accurately, achieving a 96.8% accuracy rate in face recognition while maintaining high-speed filtering. Batch optimization techniques ensure the filtering process is completed within minutes. The React Native mobile application enhances user experience, allowing them to browse, organize, and manage detected images effectively. Experimental results confirm the feasibility of using deep learning for privacy-focused, offline face recognition, enabling efficient personal image filtering and storage.This research aims to address privacy concerns associated with cloud-based face recognition services by providing an offline solution that allows users to manage their personal images without an internet connection. Additionally, this work contributes to the growing field of AI-driven image processing by proposing a highly optimized pipeline capable of handling large datasets with minimal computational overhead.
Face Detection, Deep Learning, Python, React Native, OpenCV, Mobile Application, AI, Image Filtering, Computer Vision, Privacy-Preserving AI.
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