A COMPREHENSIVE EVALUATION OF FACE RECOGNITION SOFTWARE: BALANCING TOTAL COST OF OWNERSHIP, ACCURACY AND SPEED

Abstract

As face recognition technology continues to play a pivotal role in various domains, selecting an optimal software solution becomes imperative. This paper thoroughly analyzes face recognition algorithms, emphasizing a holistic assessment of implementing them that includes the overall cost of ownership, accuracy, and speed. The research aims to guide decision-makers in choosing a solution that balances these three critical factors. The study employs a rigorous methodology and evaluates various intrusion detection solutions across multiple industries. It examines the cost of ownership comprehensively, including initial investment, maintenance expenses, and potential hidden costs. A cost-benefit analysis is conducted to unveil the true economic implications of each implementation. The accuracy of the face recognition algorithms which is the core of intrusion detection systems is assessed through the learnings from academia and real-world feedback from the security industry. Furthermore, the paper examines the critical aspect of speed, recognizing its important role in real-time applications. The evaluation considers the processing speed of each software solution, considering its responsiveness to various environments and working with large-scale datasets. This research provides a comprehensive overview of face recognition algorithms, offering small business decision-makers valuable insights to make informed choices for their intrusion detection solutions. By combining the total cost of ownership, accuracy, and speed, organizations can select a solution that aligns with their specific needs and maximizes the return on investment in intrusion detection technology.