Real-time face detection and local binary patterns histograms based face recognition on Raspberry Pi with OpenCV

• Published in: International journal of reconfigurable and embedded systems Vol. 14; no. 2; p. 527
• Main Authors: Chandrasekaran, Bharanidharan, Karunkuzhali, D., Kandasamy, V., DIllibabu, M., Rama Devi, K.
• Format: Journal Article
• Language: English
• Published: 01.07.2025
• ISSN: 2089-4864; 2722-2608
• DOI: 10.11591/ijres.v14.i2.pp527-537

This paper presents a practical end-to-end paper demonstrating real-time face recognition using a Raspberry Pi and open source computer vision library (OpenCV) consisting of three main stages: training the recognizer, real-time recognition, and face detection and data gathering. The paper offers a comprehensive guide for enthusiasts venturing into computer vision and facial recognition. Employing the Haar Cascade classifier for accurate face detection and the local binary patterns histograms (LBPH) face recognizer for robust training and recognition, the paper ensures a thorough understanding of key concepts. The step-by-step process covers software installation, camera testing, face detection, data collection, training, and real time recognition. With a focus on the Raspberry Pi platform, this paper serves as an accessible entry point for exploring facial recognition technology. Readers will gain insights into practical implementation, making it an ideal resource for learners and hobbyists interested in delving into the exciting realm of computer vision.