Open-Source Face Recognition Frameworks: A Review of the Landscape
David Wanyonyi, Turgay Çelik
Abstract
From holistic low-dimension feature-based segmentation to deep polynomial neural networks, Face Recognition (FR) accuracy has increased dramatically since its early days. The advancement and maturity of open-source FR frameworks have contributed to this trend, influencing many open-source research publications available in the public domain. The availability of modern accelerated computing capabilities through Graphics Process Unit (GPU) technology has played a substantial role in advancing open-source FR capabilities. The evolution and success of the open-source DL algorithms on FR, leveraging GPU technologies, have benefited from open datasets, resulting in many FR open-source implementations. This paper reviews the landscape of open-source FR frameworks, covering components of the FR pipeline across open datasets, face detection, face alignment, face representation, identification and verification, and deployment environments. We also discuss the current challenges and emerging directions in FR research.