Machine Learning Assisted Self‐Powered Identity Recognition Based on Thermogalvanic Hydrogel for Intelligent Security

Abstract 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 H 2 O/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.