Machine Learning for Pattern Recognition

Super-Sparse Biometrics

Many biometric recognition systems require using a small set of reference templates per client to save memory and computational resources during client verification and identification. However, both the reference templates and the combination of the corresponding matching scores are often heuristically chosen. Under this scenario, we are investigating a well-grounded approach, capable of outperforming state-of-the-art methods both in terms of recognition accuracy and number of required reference templates, by jointly learning an optimal combination of matching scores and the corresponding subset of templates.