Electrocardiogram: A Burgeoning Biometric Modality for Automated Gender Recognition

Abstract

Recent studies has examined the viability of obtain- ing secondary identifiers from core biometric traits including the iris, face, and fingerprints. This type of supplemental data, referred to as soft biometrics, includes personal traits e.g., age, gender, ethnicity, height, weight etc. Soft biometric attributes can be utilised in a variety of scenarios, such as monitoring and indexing biometric databases, enhancing the performance of pri- mary biometric systems, and providing qualitative descriptions of an individual's qualities. It is especially helpful for bridging the gap that exists between how people and machines describe biometric data. In this work, we present an introduction of soft biometrics and describe existing methodologies to extract one of the soft biometrics, namely gender. In addition, a taxonomy for recognising gender based on numerous soft biometric factors is offered, along with a listing of the advantages and disadvantages of using these qualities within the context of a gender recognition system. Additionally, the practicality of using ECG to determine gender is studied. In conclusion, we discuss potential applications, challenges, and future approaches for gender recognition using ECG.