|Face Recognition (FR) is an important area in computer vision with many applications such as security and automated border controls. The recent advancements in this domain have pushed the performance of models to human-level accuracy. However, the varying conditions in the real-world expose more challenges for their adoption. In this paper, we investigate the performance of these models. We analyze the performance of a cross-section of face detection and recognition models. Experiments were carried out without any preprocessing on three state-of-the-art face detection methods namely HOG, YOLO and MTCNN, and three recognition models namely, VGGface2, FaceNet and Arcface. Our results indicated that there is a significant reliance by these methods on preprocessing for optimum performance.|
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