We propose to improve biometric authentication performance by incorporating multimodal data quality analysis into a commercially practical embodiment of an extremely high-performance multimodal biometric verification and/or identification solution. We will utilize our proven multispectral imaging platform to create a whole-hand sensor that incorporates four modalities: five fingerprints, a palmprint, chromatic texture, and handshape. Our proposal includes a unifying method for creating a quality metric independent of modality and a method for combining quality and match information into a single value for each modality. Because the single value has the same meaning across modalities, the values can be combined using any number of methods such as the sum or maximum value. Collecting multiple biometrics with a single insertion reduces the complexity of the hardware and software integration in addition to creating a simpler system for user interaction. A single method for creating quality metrics using data-driven techniques creates a unified method for assessing quality across biometric modalities. Fusion of the multiple biometrics with their respective quality metrics creates a more robust matching metric.
Benefits:This research will result in a multimodal whole-hand biometric system with exceptional authentication performance. Commercialization will result in enhanced security in numerous access control situations such as borders, ports and airports, bio-labs, nuclear facilities, chemical plants, refineries, and anywhere access control is critical to overall system security.
Keywords:biometric, multimodal, quality metric, fingerprint, hand geometry, access control, multispectral, chromatic texture
