A systematic review on Evaluation of Existing Face Eyes Detection Algorithms for Monitoring Systems

Abstract

Among the many issues facing the world, the issue of traffic accidents, many security facilities have developed research on fingerprints, palmistry biometrics and other biometrics. In modern technologies, facial and eye detection algorithms have been developed and identified. This paper includes comparisons of face and eye tracking methods, which are extremely useful for identifying individuals in a variety of situations. Facial biometrics are more stable due to their distinct features, which gives them an advantage over other biometrics such as palmistry and fingerprint. This work dealt with 74 studies of face and eye detection and a comparison of algorithms that were used in previous researches, where the Viola-jones algorithm was identified as the best facial algorithm in terms of accuracy up to 98% for different types of face databases because this algorithm depends on geometry of face and also able to detect all faces in the same image, Furthermore, it has been determined that SVM is the best eye detection algorithm due to the accuracy of its characteristics and the speed of execution in real time so, the highest accuracy achieved was 98% accurate .

Classification of Ethnicity Using Efficient CNN Models on MORPH and FERET Datasets Based on Face Biometrics

“…Known as "soft biometrics", the features and information extracted from a person have been applied in various fields, including law enforcement, surveillance videos, advertising, and social media profiling, to improve recognition performance and the search engine for facial images. The authors of [6] discovered that there was no mention of the Arab world in relevant papers or Arab datasets. To identify these labels using deep-learning methodologies, the authors set out to generate an Arab dataset and properly label Arab sub-ethnic groups.…”

“…The face is the part of the human body that contains the most semantic information about an individual. Convolutional neural networks (CNNs) are increasingly being used to solve problems such as face recognition [2] and verification [3], gender recognition [4], aging prediction [5], and facial emotion recognition [6]. Ethnicity recognition, which is the ability of a system to discern whether an individual belongs to one of the ethnic groups based on facial appearance observations, has not received the same attention from the scientific community.…”

“…Facial soft biometrics such as age, gender, and expression have seen a boom in research using deep neural networks, but ethnicity has not had the same focus in that field of study. In addition, gathering new ethnicity datasets is not an easy task; it must be carried out manually by people trained to recognize the basic ethnicity groups using somatic facial features [6,8]. More than 3,000,000 face images have been annotated within four ethnicity groups, namely African American, East Asian, Caucasian Latin, and Asian Indian, to cover this gap in the facial soft biometrics analysis of the VGGFace2 Mivia Ethnicity Recognition (VMER) dataset [6].…”

“…In addition, gathering new ethnicity datasets is not an easy task; it must be carried out manually by people trained to recognize the basic ethnicity groups using somatic facial features [6,8]. More than 3,000,000 face images have been annotated within four ethnicity groups, namely African American, East Asian, Caucasian Latin, and Asian Indian, to cover this gap in the facial soft biometrics analysis of the VGGFace2 Mivia Ethnicity Recognition (VMER) dataset [6]. With the help of three people from diverse ethnic backgrounds, the final annotations can be derived free from the well-known other-race effect [1,2].…”

A Review of Face Detection Methods Based on Feature Approach

“…al. [33] proposed a systematic review of the evaluation of monitoring systems for driver fatigue based on the existing algorithm for detecting face /eyes. This work dealt with 74 eye and face detection studies and an algorithm comparison that was used in previous research.…”