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access icon free Fingerprint image quality assessment based on BP neural network with hierarchical clustering

© The Institution of Engineering and Technology
Received 26/01/2019, Accepted 15/11/2019, Revised 10/10/2019, Published 15/11/2019

Fingerprint image quality assessment is important because the good performance of the minutiae-based matching algorithm is heavily dependent on fingerprint images with high quality. Many efforts have been made in existing methods, but most methods either use full fingerprint images or use local areas and involve subjective judgments. Unlike previous methods, the proposed method considers both local and global assessments. Local feature vectors are extracted from the fingerprint image block for hierarchical clustering, and the results are used as target outputs of the back-propagation (BP) neural network without any subjective judgments. Global feature vectors based on the local quality assessment results are used for hierarchical clustering and fed into the BP neural network that calculates the overall error rate of genuine and imposter errors to achieve global quality assessment. Furthermore, the minutiae quality assessment method is also proposed and incorporated into the minutiae-based matching algorithm. The experimental results based on the FVC2002 and FVC2004 databases show that the proposed methods can effectively assess the quality of fingerprint images and ensure the overall improvement of matching performance.

Inspec keywords: fingerprint identification; feature extraction; image matching; neural nets; backpropagation

Other keywords: hierarchical clustering; global quality assessment; local assessments; global feature vectors; local quality assessment results; fingerprint image quality assessment; subjective judgments; fingerprint images; BP neural network; local feature vectors; minutiae quality assessment method; back-propagation neural network; global assessments; fingerprint image block; minutiae-based matching algorithm

Subjects: Neural computing techniques; Computer vision and image processing techniques; Image recognition

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