Beyond ID cards, several biometric modalities could significantly benefit from the application of Few-Shot Learning (FSL) for presentation attack detection. One prominent modality is facial recognition, where FSL can be employed to detect spoofing attempts using photographs, videos, or masks. By training on a limited number of genuine and spoofed facial images, FSL can help systems quickly adapt to new types of spoofing attacks, such as those involving high-resolution images or 3D masks.
Another modality is fingerprint recognition. FSL can be utilized to identify presentation attacks involving fake fingerprints created from silicone or other materials. By training on a small number of genuine and counterfeit fingerprints, the model can learn to distinguish between authentic and fraudulent submissions, even when faced with novel attack methods.
Iris recognition is also a promising area for FSL application. Presentation attacks in this domain may involve high-quality printed images of irises or contact lenses designed to mimic genuine iris patterns. FSL can help develop robust detection systems that require minimal training data to recognize these sophisticated attacks.
Voice recognition systems could also leverage FSL for detecting presentation attacks, such as voice synthesis or deepfake audio. By training on a limited number of authentic voice samples, the model can learn to identify discrepancies in voice patterns that indicate manipulation.
Lastly, behavioral biometrics, such as keystroke dynamics or gait analysis, could benefit from FSL. By training on a small number of samples from legitimate users, the model can learn to detect anomalies that suggest fraudulent behavior, even when the attacker attempts to mimic legitimate patterns.