Collecting fingerprints for recognition using mobile phone cameras

We present in this paper a sample quality control approach for the case using a mobile phone's camera as a fingerprint sensor for fingerprint recognition. Our approach directly estimates the maximum ridge frequency orientation by the amplitude-frequency features of the Fast Fourier Transform and takes the frequency features' difference in two perpendicular orientations as a distinguishing feature for ridge-like patterns. Then a decision criterion which combines the frequency components' energy and ridge orientation features is used to determine if an image block should be classified as high-quality fingerprint area or not. The number of such high-quality blocks can thus be used to indicate the whole fingerprint sample's quality. Experiments show this approach's effectiveness in distinguishing the high-quality blocks from other low-quality ones or background area. Mapping the quality metric to the sample utility as derived from the the NIST minutiae extractor "mindtct" function is also given to verify the approach's quality prediction effectiveness.