later recognizing faces with obstructions such as
sunglasses and masks. Initially, Amarapur and Patil
proposed a relatively simple conventional accessible
face identification method on the basis of facial
geometric features, but the accuracy of this method is
relatively low. Subsequently, Mu and Yan proposed
a face recognition method based on algebraic
features, which is relatively more tolerant to changes
in light and facial expressions (Yanmei, 2016). After
that Liao and Gu proposed a subspace-based face
recognition method SESRC&LDF, which further
improves the accuracy of unobstructed face
recognition (Malassiotis, 2005). After that, several
scientists proposed a face recognition method which
is based on bimodal fusion, which is a state of the art
occlusion-free face verification method (Guan,
2010). In recent years, due to the prevalence of new
coronaviruses, people wear masks more frequently in
their daily life. As a result, there is an increasing
demand for the mask-based face identification
technology by the society, which promotes the
enhancement of the mask-based face recognition
technology. Guan et al. have proposed a new exterior-
based face verification method, tensor subspace
regression (TSR) (Li, 2013). Li et al. have proposed a
structural coding-based method to further improve
the precision rate of masked face identification
(Kunming, 2005). As the technology developed
further, the "shallow" feature extraction based
method proposed by Li et al. improved the speed of
masked face recognition (Prasad, 2020). The deep
learning based face recognition method proposed by
Prasad et al. is among the most widely used face
verification methods (Adjabi, 2020).
The primary objective of this study is to
categorize face recognition methods into two distinct
categories based on their practical usage scenarios:
unobstructed face recognition and obstructed face
recognition. Additionally, this study aims to
summarize the key methods associated with each
category, tracing their developmental history. In a
more detailed analysis, first, unobstructed face
recognition is categorized into traditional
unobstructed face recognition and modern
unobstructed face recognition based on its
development history. Similarly, this study classifies
covert face recognition into traditional concealed face
recognition and modern concealed face recognition.
Next, the core technologies contained in these four
categories are analyzed and introduced respectively.
Finally, this study discusses the advantages and
disadvantages of the key technologies contained
within face recognition as well as the prospects for
future development. This study fills the gap of sorting
out and learning about face recognition technologies
from a practical point of view, and provides a
reference for selecting appropriate face recognition
systems for real-life individuals as well as enterprises.
This chapter begins with a background
introduction and a review of previous work in related
fields, along with a brief description of the research
objectives and methodology of this paper. Secondly,
chapter 2 introduces the core concepts as well as the
principles of the approaches taken in each
classification according to the categorization of
occluded face recognition and unoccluded face
recognition in the order of development. After that,
the results of the study are analyzed and discussed in
Chapter 3. Finally, a summary of the entire study is
presented in Chapter 4.
2 METHODOLOGIES
2.1 Dataset Description and
Preprocessing
The main datasets involved in this study include the
Yale Face Database B, the Olivetti Research
Laboratory (ORL) face dataset, the Augmented
Reality (AR) face database, and the Multi Modal
Verification for Teleservices and Security
(XM2VTS) face database. The Yale Face Database B
contains approximately 5,760 facial images of
subjects in different lighting conditions and different
postures. The ORL face dataset was created by
Olivetti's lab in Cambridge, United Kingdom, and
contains 400 facial images in Portable Gray Map
(PGM) format taken by 40 different subjects at
different times, under different lighting, different
facial conditions, and different facial details. These
images all have the same height and width. The AR
face database contains more than 4000 face images
from 126 different subjects, about 26 images per
person, in 24-bit color at 576*768 pixels. The images
in this database are frontal face images with
variations in expression, lighting, and occlusion. The
XM2VTS database is derived from the European
Union's Ability and Competence Test System
(ACTS) program, which handles access control and
thus improves the efficiency of access through the use
of multimodal recognition of faces. The database
contains frontal face images of 295 subjects when
they are speaking and when they are rotating their
heads (Li, 2018).