Automatic Cropping Fingermarks: Latent Fingerprint Segmentation

We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet. SegFinNet takes a latent image as an input and outputs a binary mask highlighting the friction ridge pattern. Our algorithm combines fully convolutional neural network and detection-based approach to process the entire input latent image in one shot instead of using latent patches. Experimental results on three different latent databases (i.e. NIST SD27, WVU, and an operational forensic database) show that SegFinNet outperforms both human markup for latents and the state-of-the-art latent segmentation algorithms. Our latent segmentation algorithm takes on average 457 (NIST SD27) and 361 (WVU) msec/latent on Nvidia GTX Ti 1080 with 12GB memory machine. We show that this improved cropping, in turn, boosts the hit rate of a latent fingerprint matcher.

[1]  Manhua Liu,et al.  Latent fingerprint segmentation based on linear density , 2016, 2016 International Conference on Biometrics (ICB).

[2]  C.-C. Jay Kuo,et al.  Adaptive Directional Total-Variation Model for Latent Fingerprint Segmentation , 2013, IEEE Transactions on Information Forensics and Security.

[3]  Vutipong Areekul,et al.  Latent fingerprints segmentation based on Rearranged Fourier Subbands , 2015, 2015 International Conference on Biometrics (ICB).

[4]  Anil K. Jain,et al.  Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge , 2017, 2018 International Conference on Biometrics (ICB).

[5]  Iasonas Kokkinos,et al.  Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.

[6]  Anil K. Jain,et al.  Latent Fingerprint Matching , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Bir Bhanu,et al.  Latent Fingerprint Image Segmentation Using Deep Neural Network , 2017 .

[8]  Anil K. Jain,et al.  Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine RidgeStructure Dictionary , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Michael D. Garris,et al.  NIST Special Database 27 Fingerprint Minutiae From Latent and Matching Tenprint Images , 2000 .

[10]  Anil K. Jain,et al.  Automated Latent Fingerprint Recognition , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Andrew Zisserman,et al.  Spatial Transformer Networks , 2015, NIPS.

[12]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Arun Ross,et al.  50 years of biometric research: Accomplishments, challenges, and opportunities , 2016, Pattern Recognit. Lett..

[15]  Anil K. Jain,et al.  Automatic segmentation of latent fingerprints , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[16]  Jiankun Hu,et al.  Latent fingerprint segmentation based on convolutional neural networks , 2017, 2017 IEEE Workshop on Information Forensics and Security (WIFS).