  As the advancement of information security, human recognition as its core
technology, has absorbed an increasing amount of attention in the past few
years. A myriad of biometric features including fingerprint, face, iris, have
been applied to security systems, which are occasionally considered vulnerable
to forgery and spoofing attacks. Due to the difficulty of being fabricated,
electrocardiogram (ECG) has attracted much attention. Though many works have
shown the excellent human identification provided by ECG, most current ECG
human identification (ECGID) researches only focus on rest situation. In this
manuscript, we overcome the oversimplification of previous researches and
evaluate the performance under both exercise and rest situations, especially
the influence of exercise on ECGID. By applying various existing learning
methods to our ECG dataset, we find that current methods which can well support
the identification of individuals under rests, do not suffice to present
satisfying ECGID performance under exercise situations, therefore exposing the
deficiency of existing ECG identification methods.
