The field of face recognition has made tremendous strides, yet challenges remain in accurately identifying faces under varying pose conditions. A recent study delves into the multi-pose effects on a FaceNet-based face recognition system, shedding light on how pose variations impact recognition performance, particularly in forensic applications.
Face recognition systems often rely on feature distances to match probe images against a gallery database. However, factors such as yaw and pitch angles significantly influence these distances, creating challenges in scenarios where face angles deviate from the frontal pose. The study examines both interpersonal (between different individuals) and intrapersonal (within the same individual) feature vector distance variations, using the CAS-PEAL Chinese face database for simulations.
Key findings include:
- Pose variation significantly affects recognition accuracy, especially at larger angles (e.g., ±67°).
- For smaller yaw angles (±15°), recognition performance is notably better, even with larger gallery databases.
- Monte Carlo simulations reveal that to achieve a 90% hit probability for near-profile poses, investigators must consider the top 35 candidates in small gallery databases. However, accuracy declines as database size increases.
The research highlights the importance of understanding pose-induced variations and suggests potential improvements in face recognition algorithms for better handling of multi-pose scenarios. While FaceNet served as the baseline in this study, future work could explore newer models and integrate face frontalization techniques to enhance performance.
This study underscores the need for continuous innovation in face recognition systems to address real-world challenges effectively.
Full Text: https://www.igminresearch.com/articles/html/igmin231
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