Title: A common convolutional neural network model to classify plain, rolled and latent fingerprints
Authors: Asif Iqbal Khan; M. Arif Wani
Addresses: Department of Computer Science, University of Kashmir, India ' Department of Computer Science, University of Kashmir, India
Abstract: Fingerprint classification helps in reducing the number of comparisons during the matching stage in automatic fingerprint identification system. In this study, a convolutional neural network model is proposed for classification of plain, rolled and latent fingerprints. We first propose a new convolutional neural network model initialised with random weights and train the model on fingerprint images. Then we fine-tune two pre-trained convolutional neural network models on fingerprint images. Finally, we compare these three models: two pre-trained models and a custom convolutional neural network model initialised with random weights. We show that pre-trained models can achieve the state-of-the-art results on other similar tasks with no or little fine-tuning. We also show that training data size and depth of the network have a serious impact on the overall performance of deep networks. Dropout is used to enhance the performance of deep networks where the labelled training data is not of sufficient size. All the three models trained on NIST DB4 fingerprint and IIIT-D latent fingerprint databases report good accuracy. By only fine-tuning the pre-trained convolutional neural network model, we get the accuracy of 99%, easily out-performing the state-of-the-art.
Keywords: convolutional neural network; CNN; deep learning; fingerprint classification; ConvNet.
International Journal of Biometrics, 2019 Vol.11 No.3, pp.257 - 273
Available online: 23 May 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article