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As the advancement of information security, human recognition as its coretechnology, has absorbed an increasing amount of attention in the past fewyears. A myriad of biometric features including fingerprint, face, iris, havebeen applied to security systems, which are occasionally considered vulnerableto forgery and spoofing attacks. Due to the difficulty of being fabricated,electrocardiogram (ECG) has attracted much attention. Though many works haveshown the excellent human identification provided by ECG, most current ECGhuman identification (ECGID) researches only focus on rest situation. In thismanuscript, we overcome the oversimplification of previous researches andevaluate the performance under both exercise and rest situations, especiallythe influence of exercise on ECGID. By applying various existing learningmethods to our ECG dataset, we find that current methods which can well supportthe identification of individuals under rests, do not suffice to presentsatisfying ECGID performance under exercise situations, therefore exposing thedeficiency of existing ECG identification methods.

ECG Identification under Exercise and Rest Situations via Various Learning Methods