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Fingerprints are one of the most copious evidence in a crime scene and, forthis reason, they are frequently used by law enforcement for identification ofindividuals. But fingerprints can be altered. "Altered fingerprints", refers tointentionally damage of the friction ridge pattern and they are often used bysmart criminals in hope to evade law enforcement. We use a deep neural networkapproach training an Inception-v3 architecture. This paper proposes a methodfor detection of altered fingerprints, identification of types of alterationsand recognition of gender, hand and fingers. We also produce activation mapsthat show which part of a fingerprint the neural network has focused on, inorder to detect where alterations are positioned. The proposed approachachieves an accuracy of 98.21%, 98.46%, 92.52%, 97.53% and 92,18% for theclassification of fakeness, alterations, gender, hand and fingers, respectivelyon the SO.CO.FING. dataset.

Single architecture and multiple task deep neural network for altered fingerprint analysis