CNNAI: A Convolution Neural Network-Based Latent Fingerprint Matching Using the Combination of Nearest Neighbor Arrangement Indexing

• Main Authors: Uttam U. Deshpande, V. S. Malemath, Shivanand M. Patil, Sushma V. Chaugule
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-09-01
• Series: Frontiers in Robotics and AI
• Subjects: convolution neural network (CNN); CNNAI; global features; nearest neighbor; indexing; feature vector
• Online Access: https://www.frontiersin.org/article/10.3389/frobt.2020.00113/full

Automatic fingerprint identification systems (AFIS) make use of global fingerprint information like ridge flow, ridge frequency, and delta or core points for fingerprint alignment, before performing matching. In latent fingerprints, the ridges will be smudged and delta or core points may not be available. It becomes difficult to pre-align fingerprints with such partial fingerprint information. Further, global features are not robust against fingerprint deformations; rotation, scale, and fingerprint matching using global features pose more challenges. We have developed a local minutia-based convolution neural network (CNN) matching model called “Combination of Nearest Neighbor Arrangement Indexing (CNNAI).” This model makes use of a set of “n” local nearest minutiae neighbor features and generates rotation-scale invariant feature vectors. Our proposed system doesn't depend upon any fingerprint alignment information. In large fingerprint databases, it becomes very difficult to query every fingerprint against every other fingerprint in the database. To address this issue, we make use of hash indexing to reduce the number of retrievals. We have used a residual learning-based CNN model to enhance and extract the minutiae features. Matching was done on FVC2004 and NIST SD27 latent fingerprint databases against 640 and 3,758 gallery fingerprint images, respectively. We obtained a Rank-1 identification rate of 80% for FVC2004 fingerprints and 84.5% for NIST SD27 latent fingerprint databases. The experimental results show improvement in the Rank-1 identification rate compared to the state-of-art algorithms, and the results reveal that the system is robust against rotation and scale.