Gender prediction system through behavioral biometric handwriting: a comprehensive review

Identification of a person on the basis of different characteristics is a prevailing area of research. Both behavioral biometrics and physical biometrics are used as measures to recognize a person's identity. Physical biometrics includes fingerprints and IRIS patterns; whereas behavioral biometrics includes some sort of pattern in human activities like handwriting. Handwriting, like other biometrics, is one of the best attributes for implicitly identifying a person. Every person has a different style of handwriting. Gender prediction on the basis of handwriting styles in different Indian and non-Indian scripts offers a vast area for research and is an effective strategy for biometrics. This paper's major goal is to give an in-depth analysis of gender prediction using handwriting in non-Indic and Indic scripts. The intention is to provide a variety of feature extraction methods, datasets available, and a taxonomy of conventional and machine learning-based tools for gender prediction on the basis of handwriting. This article discusses the context, survey protocol, methodology, and various datasets used by the various researchers. The compiled study used for feature extraction and classification methods, along with a critical analysis of the work done, is also elaborated in this manuscript.