Evaluation of Deep Learning Methods in Face Recognition: Datasets, Metrics, and Results

Author(s)	Mr. Saritha Kishore, Ms. Padmavathi H G, Ms. Sowmyshree C S, Ms. Manjula K B
Country	India
Abstract	: Face recognition has become a cornerstone technology in biometric authentication, surveillance, and social media applications. With the advent of deep learning, particularly convolutional neural networks (CNNs), face recognition systems have seen unprecedented improvements in accuracy, robustness, and scalability. This paper presents a comprehensive evaluation of deep learning methods applied to face recognition, focusing on three critical aspects: datasets, performance metrics, and empirical results. we analyze and compare the performance of state-of-the-art deep learning models such as DeepFace, FaceNet, SphereFace, CosFace, and ArcFace across these datasets. The results demonstrate that while current models achieve near-perfect accuracy on constrained datasets, challenges persist in real-world scenarios due to variations in pose, illumination, occlusion, and demographic diversity.
Keywords	Convolutional Neural Networks (CNNs), DeepFace, FaceNet, SphereFace, CosFace, and ArcFace, HOG, OpenCV, RON
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-10-31
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9116
Short DOI	https://doi.org/g99qm4

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