Identity recognition as the first barrier of intelligent security plays a vital role, which is facing new challenges that are unable to meet the need of intelligent era due to low accuracy, complex configuration and dependence on power supply. Here, a finger temperature‐driven intelligent identity recognition strategy is presented based on a thermogalvanic hydrogel (TGH) by actively discerning biometric characteristics of fingers. The TGH is a dual network PVA/Agar hydrogel in an H2O/glycerol binary solvent with [Fe(CN)6]3−/4− as a redox couple. Using a concave‐arranged TGH array, the characteristics of users can be distinguished adequately even under an open environment by extracting self‐existent intrinsic temperature features from five typical sites of fingers. Combined with machine learning, the TGH array can recognize different users with a high average accuracy of 97.6%. This self‐powered identity recognition strategy is further applied to a smart lock, attaining a more reliable security protection from biometric characteristics than bare passwords. This work provides a promising solution for achieving better identity recognition, which has great advantages in intelligent security and human‐machine interaction toward future Internet of everything.

中文翻译：

---

基于热电水凝胶的机器学习辅助自供电身份识别，实现智能安全


身份识别作为智能安防的第一道屏障，发挥着至关重要的作用，但由于准确率低、配置复杂、对电源的依赖等原因，身份识别正面临着新的挑战，无法满足智能时代的需求。这里，通过主动识别手指的生物特征，提出了一种基于热电水凝胶（TGH）的手指温度驱动的智能身份识别策略。 TGH 是在 H2O/甘油二元溶剂中的双网络 PVA/琼脂水凝胶，以 [Fe(CN)6]3−/4− 作为氧化还原对。利用凹面排列的TGH阵列，通过从手指的五个典型部位提取自存在的固有温度特征，即使在开放的环境下也可以充分区分用户的特征。结合机器学习，TGH阵列可以识别不同的用户，平均准确率高达97.6%。这种自驱动的身份识别策略进一步应用于智能锁，通过生物特征获得比裸密码更可靠的安全保护。这项工作为实现更好的身份识别提供了一种有前途的解决方案，在智能安全和人机交互方面对未来的万物互联具有巨大的优势。