In Your Face: Person Identification Through Ratios and Distances Between Facial Features

Abstract

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.

Design for Visitor Authentication Based on Face Recognition Technology Using CCTV

“…In addition, despite being a family member, a False Rejection Rate (FRR) may occur when a face is obscured by a mask, glasses, or other facial covering and some of the vector values of feature points are distorted. Table 3 shows the evaluation indicators, FAR, FRR, and Precision according to the threshold of similarity in the HoG algorithm using Dlib [30]. Thus, because there is FRR and FAR, additional authentication methods are required.…”

“…Additional research is now being conducted to confirm the approach of a user with the PIR proximity sensor, or to recognize the user's voice with a microphone, and identify the user with additional authentication methods such as questions and answers [30]. Face recognition technology using deep learning is being used for user authentication by recognizing people in access management systems.…”

“…This function is necessary to ensure that when a visitor enters a password into the door lock, no one is peeking from behind. The proposed system can identify the number of visitors through CCTV and protect against peeking, which is a social engineering attack [30].…”

Perception of Facial Impressions Using Explicit Features of the Face (xFoFs)

“…Alsawwaf et al [10] presented a scheme that measures the similarity of two human faces. For this purpose, they detected sibling landmarks, calculated landmark-based feature values and compared the similarities of two faces.…”

“…To provide a quantitative measurement of a facial impression, we conducted a comprehensive review of anthropometry studies [4,6,7,[9][10][11][12][13][14][15]18,19,23,25,28] and collected 68 facial features, eliminating any duplicates. These features are suggested in Appendix A.…”