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In recent years, biometrics (e.g., fingerprint or face recognition) has replaced traditional passwords and PINs as a widely used method for user authentication, particularly in personal or mobile devices. Differing from state-of-the-art biometrics, heart biometrics offer the advantages of liveness detection, which provides strong tolerance to spoofing attacks. To date, several authentication methods primarily focusing on electrocardiogram (ECG) have demonstrated remarkable success; however, the degree of exploration with other cardiac signals is still limited. To this end, we discuss the challenges in various cardiac domains and propose future prospectives for developing effective heart biometrics systems in real-world applications. 

Free-text keystroke is a form of behavioral biometrics which has great potential for addressing the security limitations of conventional one-time authentication by continuously monitoring the user's typing behaviors. This paper presents a new, enhanced continuous authentication approach by incorporating the dynamics of both keystrokes and wrist motions. Based upon two sets of features (free-text keystroke latency features and statistical wrist motion patterns extracted from the wrist-worn smartwatches), two one-vs-all Random Forest Ensemble Classifiers (RFECs) are constructed and trained respectively. A Dynamic Trust Model (DTM) is then developed to fuse the two classifiers' decisions and realize non-time-blocked real-time authentication. In the free-text typing experiments involving 25 human subjects, an imposter/intruder can be detected within no more than one sentence (average 56 keystrokes) with an FRR of 1.82% and an FAR of 1.94%. Compared with the scheme relying on only keystroke latency which has an FRR of 4.66%, an FAR of 17.92% and the required number of keystroke of 162, the proposed authentication system shows significant improvements in terms of accuracy, efficiency, and usability.