Comparing the Effectiveness and Performance of Image Processing Algorithms in Face Recognition

Many institutions have recently embraced biometric security solutions, utilizing biological measurements to safeguard against fraudulent activities, theft, and various security threats. Face recognition technology holds a pivotal role within the realm of bio-metric security systems, serving purposes such as authentication, monitoring, individual identification, and identity verification. This article aims to delve into the examination of facial recognition systems grounded in deep learning. This focus arises due to the intricate nature of the process and the existence of numerous hurdles and variables that impact algorithm performance. The objective here is to illuminate the foremost challenges that real-world systems encounter, often overlooked in previous research. Additionally,under these challenges, the article will conduct a comparative analysis of the performance of prominent facial recognition algorithms, namely VGGFace, FaceNet, and ArcFace. This academic approach will allow to make informed choices when selecting the most suitable algorithms for specific applications.