Abstract：
Unconstrained palmprint images have shown great potential for recognition applications due to their lower restrictions regarding hand poses and backgrounds during contactless image acquisition. However, they face two challenges: 1) unclear palm contours and finger-valley points of unconstrained palmprint images make it difficult to locate landmarks to crop the palmprint region of interest (ROI); and 2) large intra-class diversities of unconstrained palmprint images hinder the learning of intra-class-invariant palmprint features. In this paper, we propose to directly extract the complete palmprint region as the ROI (CROI) using the detection-style CenterNet without requiring the detection of any landmarks, and large intra-class diversities may occur. To address this, we further propose a palmprint feature alignment and learning hybrid network (PalmALNet) for unconstrained palmprint recognition. Specifically, we first exploit and align the multi-scale shallow representation of unconstrained palmprint images via deformable convolution and alignment-aware supervision, such that the pixel gaps of the intra-class palmprint CROIs can be minimized in shallow feature space. Then, we develop multiple triple-attention learning modules by integrating spatial, channel, and self-attention operations into convolution to adaptively learn and highlight the latent identity-invariant palmprint information, enhancing the overall discriminative power of the palmprint features. Extensive experimental results on four challenging palmprint databases demonstrate the promising effectiveness of both the proposed PalmALNet and CROI for unconstrained palmprint recognition.
摘要：
无约束的掌纹图像由于在非接触式图像采集过程中对手部姿势和背景的限制较低，因此在识别应用中显示出巨大的潜力。然而,他们面临两个挑战：1）不清晰的手掌轮廓和不受约束的掌纹图像的指谷点使得难以定位地标以裁剪感兴趣的掌纹区域（ROI），2）无约束掌纹图像的大类内多样性阻碍了类内不变掌纹特征的学习。在本文中,我们建议使用检测风格的CenterNet直接提取完整的掌纹区域作为ROI（CROI），而不需要检测任何标志，和大的类内多样性可能发生。为了解决这个问题，我们进一步提出了一种掌纹特征对齐和学习混合网络（PalmALNet），用于无约束掌纹识别。具体来说,我们首先通过可变形卷积和对齐感知监督来利用和对齐无约束掌纹图像的多尺度浅表示，这样类内掌纹CROI的像素间隙可以在浅特征空间中最小化。然后，我们通过整合空间来开发多个三重注意力学习模块，通道,和自注意操作到卷积自适应地学习和突出潜在的身份不变的掌纹信息，增强掌纹特征的整体鉴别力。在四个具有挑战性的掌纹数据库上的大量实验结果表明，所提出的PalmALNet和CROI对于无约束掌纹识别都具有良好的有效性。
