Single architecture and multiple task deep neural network for altered fingerprint analysis
This work addresses the challenge for law enforcement in identifying individuals when fingerprints are intentionally altered by criminals, representing an incremental improvement with a multi-task approach.
The paper tackles the problem of analyzing altered fingerprints by proposing a deep neural network that simultaneously detects fakeness, identifies alteration types, and recognizes gender, hand, and fingers, achieving accuracies ranging from 92.18% to 98.46% on the SO.CO.FING. dataset.
Fingerprints are one of the most copious evidence in a crime scene and, for this reason, they are frequently used by law enforcement for identification of individuals. But fingerprints can be altered. "Altered fingerprints", refers to intentionally damage of the friction ridge pattern and they are often used by smart criminals in hope to evade law enforcement. We use a deep neural network approach training an Inception-v3 architecture. This paper proposes a method for detection of altered fingerprints, identification of types of alterations and recognition of gender, hand and fingers. We also produce activation maps that show which part of a fingerprint the neural network has focused on, in order to detect where alterations are positioned. The proposed approach achieves an accuracy of 98.21%, 98.46%, 92.52%, 97.53% and 92,18% for the classification of fakeness, alterations, gender, hand and fingers, respectively on the SO.CO.FING. dataset.