Exploring the frontiers of computer vision, machine learning, and human-computer interaction
I designed and developed a modular face detection & recognition system from scratch, focused on flexibility, scalability, and real-time performance, Multi-Detector Support ā Switch between OpenCV Haar Cascade, MTCNN, Dlib HOG/CNN, and face_recognition seamlessly View
Comprehensive study on client-side object detection and matching using modern web technologies, focusing on privacy-preserving computer vision applications.
This Research presents an in-depth, Python-based face detection pipeline utilizing OpenCV, MTCNN, Dlib, and the face_recognition library. With modular preprocessing, robust detection, and RESTful API support, this pipeline addresses various input types
We present a comprehensive face recognition pipeline that integrates modular software architecture, real-time streaming via WebRTC, and cryptographic logging for security compliance. Our system supports live webcam recognition, face enrollment, gallery management, and a novel image-based person search interface. We describe the design rationale, implementation details, and performance evaluation,