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How can biometric systems be made resilient to presentation attacks while preserving user privacy and recognition accuracy? The group researches presentation attack detection methods and spoofing defenses, including explainable attention-guided iris PAD. Researchers develop semi-adversarial networks and convolutional autoencoders to impart privacy to face images while retaining utility for recognition. The work explores vulnerabilities in fingerprint and iris systems through empirical studies that expose spoofing and masterprint risks. The lab investigates multi-biometrics and fusion strategies to improve recognition reliability under adversarial or degraded inputs.
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