Evaluating Accuracy Latency and Robustness of Face Recognition Models for Real-Time Web Applications
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Abstract
Face-recognition technology is one of the most important advancements in the field of computer vision. They play a crucial role in many applications, including biometric authentication, surveillance, online security, and interactive web systems. The use of web-based solutions is increasing continuously. Therefore, accurate and fast recognition models employing few resources are required in real-world applications. However, because of the challenges related to such environments, including the lighting, occlusion, pose, and computing power of client devices, it is difficult to ascertain which model will be most successful in a real-life scenario. The purpose of this research is to compare four deep learning frameworks for face recognition, which are most widely used by scientists and software developers. FaceNet, SFace, OpenFace, and DeepFace have all been subjected to rigorous examinations to determine which one is the most suitable for work on the real-time web. As part of the assessment, a prototype application was created to enable the simulation of real-time applications. This solution enables both the upload of the test image and the group image to determine which person is the subject of the research. Subsequently, the model performance was tested under the conditions of pose, light, and occlusion variations. Performance was measured using the following features: accuracy, similarity distance, processing latency, and robustness. Therefore, the results show that there is no single best model compatible with all web-based applications, and the outcome fundamentally depends on the developer’s required accuracy and speed.
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