A novel fingerprint smear detection method based on integrated sub-band feature representation

Fingerprint smear detection has become a challenging issue due to the erratic texture of the smear tissue and its similarity to normal finger area. This paper presents a novel fingerprint image smear detection approach integrating symmetric wavelet transform (SWT), gray level co-occurrence matrix and DCT. A feature extraction algorithm is first proposed by utilizing SWT to decompose each fingerprint and characterizing local texture features of defective finger tissue with the SWT coefficients in sub-bands 4∼19. Concurrence matrix based texture features are incorporated into the feature vector to further improve the texture classification sensitivity. The fused feature vector is then fed into a pre-trained genetic neural network classifier, which identifies smears by labeling fingerprint sub-blocks into different categories. Finally, DCT decomposition is used to detect abnormalities in smear images. Experimental results indicate that the hybrid method can effectively identify various types of fingerprint smears.