Abstract
This research aims to analyze the applicability and performance of face recognition technologies using different Python-based libraries. Face recognition and comparison tests were performed using the face_recognition, OpenCV and DeepFace libraries, respectively . First, basic face detection application was successfully implemented with the face_recognition library. Then, face recognition was performed using traditional methods using OpenCV. But limited accuracy results were obtained. Finally, deep learning-based face recognition was performed with the DeepFace library, high accuracy rates were achieved, but high processing power was required. All three methods were systematically compared in terms of hardware requirements, ease of use, processing time and accuracy rates. The findings revealed that each library has advantages and limitations for certain application scenarios. In this context, it was emphasized that the selection of the appropriate algorithm for the needs in the design of face recognition systems is critical.