Deshpande Uttam U, Malemath V S, Patil Shivanand M, Chaugule Sushma V
Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, India.
Department of Computer Science and Engineering, KLE Dr. M. S. Sheshgiri College of Engineering and Technology, Belagavi, India.
Front Robot AI. 2020 Sep 17;7:113. doi: 10.3389/frobt.2020.00113. eCollection 2020.
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.
自动指纹识别系统（AFIS）在进行匹配之前，会利用全局指纹信息，如纹线流向、纹线频率以及三角点或中心点等进行指纹对齐。在潜在指纹中，纹线会模糊不清，三角点或中心点可能无法获取。利用这样的部分指纹信息对指纹进行预对齐变得困难。此外，全局特征对指纹变形的鲁棒性不强；使用全局特征进行旋转、缩放以及指纹匹配面临更多挑战。我们开发了一种基于局部细节特征的卷积神经网络（CNN）匹配模型，称为“最近邻排列索引组合（CNNAI）”。该模型利用一组“n”个局部最近细节特征邻居，并生成旋转缩放不变特征向量。我们提出的系统不依赖于任何指纹对齐信息。在大型指纹数据库中，要将数据库中的每一个指纹与其他每一个指纹进行查询非常困难。为了解决这个问题，我们利用哈希索引来减少检索次数。我们使用了基于残差学习的CNN模型来增强和提取细节特征。分别在FVC2004和NIST SD27潜在指纹数据库上，针对640张和3758张图库指纹图像进行匹配。对于FVC2004指纹，我们获得了80%的一级识别率，对于NIST SD27潜在指纹数据库，识别率为84.5%。实验结果表明，与现有算法相比，一级识别率有所提高，结果表明该系统对旋转和缩放具有鲁棒性。
