Open AccessIn Your Face: Person Identification Through Ratios and Distances Between Facial Features
Author(s) -
Mohammad Alsawwaf,
Ze Chaczko,
Marek Kulbacki,
Nikhil Sarathy
Publication year - 2021
Publication title -
vietnam journal of computer science
Language(s) - English
Resource type - Journals
eISSN - 2196-8888
pISSN - 2196-8896
DOI - 10.1142/s2196888822500105
Subject(s) - computer science , face (sociological concept) , artificial intelligence , identification (biology) , facial recognition system , pattern recognition (psychology) , feature (linguistics) , word error rate , identity (music) , machine learning , social science , linguistics , philosophy , botany , physics , sociology , acoustics , biology
These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. This approach investigates the human face identification based on frontal images by producing ratios from distances between the different features and their locations. Moreover, this extended version includes an investigation of identification based on side profile by extracting and diagnosing the feature sets with geometric ratio expressions which are calculated into feature vectors. The last stage involves using weighted means to calculate the resemblance. The approach considers an explainable Artificial Intelligence (XAI) approach. Findings, based on a small dataset, achieve that the used approach offers promising results. Further research could have a great influence on how faces and face-profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate, and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89. This work is an extended version of the paper submitted in ACIIDS 2020.