Zhang Zhao, Liu Shuxin, Liu Manhua
Department of Instrument Science and Engineering, School of EIEE, Shanghai Jiao Tong University, China.
College of Electrical Engineering, Shanghai DianJi University, China.
Pattern Recognit. 2021 Dec;120:108189. doi: 10.1016/j.patcog.2021.108189. Epub 2021 Jul 21.
With the outbreak and wide spread of novel coronavirus (COVID-19), contactless fingerprint recognition has attracted more attention for personal recognition because it can provide significantly higher user convenience and hygiene than the traditional contact-based fingerprint recognition. However, it is still challenging to achieve a highly accurate recognition due to the low ridge-valley contrast and pose variances of contactless fingerprints. Minutiae points are a kind of ridge flow discontinuities, and robust and accurate extraction is an important step for most automatic fingerprint recognition algorithms. Most of existing methods are based on two stages which locate the minutiae points first and then compute their directions. The two-stage method cannot make full use of location and direction information. In this paper, we propose a multi-task fully deep convolutional neural network for jointly learning the minutiae location detection and its corresponding direction computation which operates directly on the whole gray scale contactless fingerprints. The proposed method consists of offline training and online testing stages. In the training stage, a fully deep convolutional neural network is built for the tasks of minutiae detection and its direction regression, with an attention mechanism to make the direction regression branch concentrate on the minutiae points. A new loss function is proposed to jointly learn the tasks of minutiae detection and its direction regression from the whole fingerprints. In the testing stage, the trained network is applied on the whole contactless fingerprint to generate the minutiae location and direction maps. The proposed multi-task leaning method performs better than the individual single task and it operates directly on the raw gray-scale contactless fingerprints without preprocessing. The results on three contactless fingerprint datasets show the proposed algorithm performs better than other minutiae extraction algorithms and the commercial software.
随着新型冠状病毒（COVID-19）的爆发和广泛传播，非接触式指纹识别因其能比传统的基于接触的指纹识别提供更高的用户便利性和卫生性而在个人识别方面受到了更多关注。然而，由于非接触式指纹的脊谷对比度低和姿态变化，实现高精度识别仍然具有挑战性。细节点是一种脊线流动的不连续点，稳健而准确的提取是大多数自动指纹识别算法的重要步骤。现有的大多数方法基于两个阶段，首先定位细节点，然后计算其方向。两阶段方法不能充分利用位置和方向信息。在本文中，我们提出了一种多任务全深度卷积神经网络，用于联合学习细节点位置检测及其相应的方向计算，该网络直接对整个灰度非接触式指纹进行操作。所提出的方法包括离线训练和在线测试阶段。在训练阶段，构建一个全深度卷积神经网络用于细节点检测及其方向回归任务，并采用注意力机制使方向回归分支专注于细节点。提出了一种新的损失函数，用于从整个指纹中联合学习细节点检测及其方向回归任务。在测试阶段，将训练好的网络应用于整个非接触式指纹，以生成细节点位置和方向图。所提出的多任务学习方法比单个单任务方法表现更好，并且它直接对原始灰度非接触式指纹进行操作而无需预处理。在三个非接触式指纹数据集上的结果表明，所提出的算法比其他细节点提取算法和商业软件表现更好。
