SURVEY ON:”EMPLOYEE MANAGEMENT SYSTEM USING DEEP LEARNING”

With the increasing demand for automated and objective employee performance evaluation, traditional attendance-based systems have become inadequate in measuring real-time engagement and productivity.This survey explores recent advancements in intelligent employee management systems that integrate Convolutional Neural Networks (CNNs) and computer vision for activity recognition and productivity analysis.Various research contributions employing video-based monitoring, frame preprocessing, and deep learning models for classifying employee behavior as "Working" or "Idle" are reviewed.The study compares model architectures, dataset utilization, and system design strategies that enable real-time monitoring and decisionmaking.Furthermore, it highlights key challenges such as data privacy, scalability, and environmental adaptability that affect deployment in organizational settings.This paper also discusses emerging trends including multimodal fusion, edge computing, and explainable AI for improved transparency and fairness.The survey concludes by identifying open research directions toward developing efficient, ethical, and scalable employee management solutions powered by computer vision.