Lightweight Quality Metrics for Smartphone Camera Based Fingerprint Samples

We present in this paper some lightweight metrics for quality assessment of fingerprint samples captured from a general-purposed optical camera using block-based autocorrelation and fast Walsh-Hadamard spectrum features. We generate a feature vector including 6 components in three categories to assess an image block´s quality: (1) gray-scale values statistics, (2) autocorrelation based features, (3) Walsh-Hadamard spectrum features from the autocorrelation result. Then a support vector machine is adopted to produce a binary decision whether the image block should be classified as high-quality fingerprint area or not. Finally our approach generates a quality score by counting the high-quality blocks in the sample image. Compared to our previous work [12] on the same topic, this paper uses a more compact feature vector and less computationally-complicated transformation. Experiments demonstrated the proposed metrics´ effectiveness.