Attendance tracking systems play a pivotal role in various domains, from educational institutions to workplaces and security applications. However, traditional systems often grapple with inefficiencies and security vulnerabilities. The integration of liveness detection in modern facial recognition systems offers a promising solution to these challenges. This research report presents the development of a Face Attendance System with Liveness Detection, employing machine learning techniques and Python as the primary programming language. The research addresses the limitations of existing attendance systems by incorporating advanced facial recognition models and robust liveness detection techniques. With the aim of enhancing attendance tracking accuracy and preventing fraudulent attempts, this system offers a comprehensive solution. The report outlines the research's objectives, methodology, and expected outcomes, highlighting the potential to revolutionize attendance management in education, workplaces, and security-sensitive environments. The fusion of facial recognition and liveness detection represents a significant step forward in improving the reliability and security of attendance systems.