Fingerprint sensor classification via Mélange of handcrafted features

Large scale biometrics projects rely on capturing images/signal from multiple sensors. For example, in India's Aadhaar project, multiple fingerprint sensors of different make and model are used for data collection. Similarly, in law enforcement applications, different agencies use different fingerprint sensors. These scenarios cause two potential problems: (i) sensor inter-operability and (ii) protecting/recording chain of evidence. While sensor inter-operability in fingerprints is a well studied problem, automatically recording chain of evidence is a relatively less explored research problem. For both the problems, one potential approach includes automatically identifying sensors based on the input image. This paper presents a novel fingerprint sensor identification algorithm based on multiple features such as Haralick, entropy, statistical and image quality features. The proposed algorithm is evaluated on a large database with 30,000 images with 15 fingerprint sensor classes. The proposed algorithm achieves an accuracy of 96% and computationally requires less than 10 milliseconds for an image.