论文标题
单个体系结构和多个任务深神经网络，用于改变指纹分析
Single architecture and multiple task deep neural network for altered fingerprint analysis
论文作者
论文摘要
指纹是犯罪现场中最丰富的证据之一，因此，执法部门经常使用它们来识别个人。但是指纹可以改变。 “指纹更改”是指故意损坏摩擦脊模式，智能犯罪分子通常会使用它们来逃避执法。我们使用深度神经网络方法培训Inception-V3体系结构。本文提出了一种检测指纹改变的方法，鉴定改变类型以及对性别，手和手指的识别。我们还产生激活图，以显示神经网络的指纹部分的哪一部分着重于指纹，以检测改变位置的位置。所提出的方法的准确度分别为98.21％，98.46％，92.52％，97.53％和92,18％，分别在so.co.fing上分别分类，改变，性别，性别，手和手指。数据集。
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.
