Investigating the impact of face categorization on recognition performance

Face recognition is a key biometric technology with a wide range of potential applications both in government and private sectors. Despite considerable progress in face recognition research over the past decade, today's face recognition systems are not accurate or robust enough to be fully deployed in high security environments. In this paper, we investigate the impact of face categorization on recognition performance. In general, face categorization can be used as a filtering step to limit the search space during identification (e.g., a person categorized as a middle-aged, Asian male, needs to be compared only to subjects having the same profile). Our experimental results demonstrate that face categorization based on important visual characteristics such as gender, ethnicity, and age offers significant improvements in recognition performance including higher recognition accuracy, lower time requirements, and graceful degradation. Additional performance improvements can be expected by implementing "category-specific" recognition subsystems that are optimized to discriminate more accurately between faces within the same face category rather than faces between other categories.