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We propose a texture template approach, consisting of a set of virtualminutiae, to improve the overall latent fingerprint recognition accuracy. Tocompensate for the lack of sufficient number of minutiae in poor quality latentprints, we generate a set of virtual minutiae. However, due to a large numberof these regularly placed virtual minutiae, texture based template matching hasa large computational requirement compared to matching true minutiae templates.To improve both the accuracy and efficiency of the texture template matching,we investigate: i) both original and enhanced fingerprint patches for trainingconvolutional neural networks (ConvNets) to improve the distinctiveness ofdescriptors associated with each virtual minutiae, ii) smaller patches aroundvirtual minutiae and a fast ConvNet architecture to speed up descriptorextraction, iii) reduce the descriptor length, iv) a modified hierarchicalgraph matching strategy to improve the matching speed, and v) extraction ofmultiple texture templates to boost the performance. Experiments on NIST SD27latent database show that the above strategies can improve the matching speedfrom 11 ms (24 threads) per comparison (between a latent and a reference print)to only 7.7 ms (single thread) per comparison while improving the rank-1accuracy by 8.9% against 10K gallery.

Latent Fingerprint Recognition: Role of Texture Template