  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 approaches 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. We further show that this improved cropping boosts the
hit rate of a latent fingerprint matcher.
