DIFFERENT FEATURES OF LIBRARIES OF PROGRAMMING LANGUAGES IN FACE RECOGNITION

Abstract

Face recognition technology has rapidly advanced due to the integration of sophisticated programming libraries across various languages. This article explores the different features of prominent libraries in Python, C++, Java, and MATLAB, focusing on their capabilities in face detection, feature extraction, and recognition accuracy. Python libraries like OpenCV, Dlib, and face_recognition offer ease of use and strong community support, while C++ libraries provide superior execution speed and control. Java-based libraries, including JavaCV and OpenIMAJ, emphasize cross-platform compatibility, whereas MATLAB provides a robust environment for algorithm prototyping and visualization. The study reviews recent research and practical applications, highlighting performance benchmarks and use cases. Comparative analysis through experimental results illustrates how language-specific libraries cater to different development needs in terms of scalability, accuracy, and real-time processing. This comprehensive overview aids developers and researchers in selecting the most appropriate library tailored to their face recognition projects.