  Fingerprint recognition is often a game-changing step in establishing
evidence against criminals. However, we are increasingly finding that criminals
deliberately alter their fingerprints in a variety of ways to make it difficult
for technicians and automatic sensors to recognize their fingerprints, making
it tedious for investigators to establish strong evidence against them in a
forensic procedure. In this sense, deep learning comes out as a prime candidate
to assist in the recognition of damaged fingerprints. In particular,
convolution algorithms. In this paper, we focus on the recognition of damaged
fingerprints by Convolutional Long Short-Term Memory networks. We present the
architecture of our model and demonstrate its performance which exceeds 95%
accuracy, 99% precision, and approaches 95% recall and 99% AUC.
