This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

本文提出了一种新颖的掌纹生物识别多任务学习框架，该框架联合优化了分类和哈希分支。我们框架内的分类分支有​​助于同时执行三个不同的任务：身份识别和软生物识别分类，包括性别和手性。另一方面，散列分支可以生成掌纹散列码，优化模板的最小存储和高效匹配。哈希分支通过合并从分类分支获取的知识，从这些任务中导出补充信息。与孤立的单个任务相比，这种方法可以带来卓越的整体性能。为了提高多任务学习的有效性，引入了两个附加模块，即注意力机制模块和定制门控制模块。这些模块对于为关键渠道分配更高的权重和促进特定任务的专家知识整合至关重要。此外，还加入了自动权重调整模块，以进一步优化学习过程。该模块微调分配给不同任务的权重，从而提高性能。集成上述三个模块在各种分类任务中显示出令人鼓舞的准确性，并且显着提高了身份验证准确性。广泛的实验结果验证了我们提出的框架的有效性。