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Face Recognition: A Novel Multi-Level Taxonomy based Survey
Alireza Sepas-Moghaddam
1*
, Fernando Pereira
1
, Paulo Lobato Correia
1
1
Instituto de Telecomunicações, Instituto Superior Técnico – Universidade de Lisboa, , Lisbon, Portugal
*
alireza@lx.it.pt
Abstract: In a world where security issues have been gaining growing importance, face recognition systems have attracted
increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment.
To help understanding the landscape and abstraction levels relevant for face recognition systems, face recognition
taxonomies allow a deeper dissection and comparison of the existing solutions. This paper proposes a new, more
encompassing and richer multi-level face recognition taxonomy, facilitating the organization and categorization of available
and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face
recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support and
feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition
solutions is presented. The paper concludes with a discussion on current algorithmic and application related challenges which
may define future research directions for face recognition.
1. Introduction
Face recognition systems have been successfully used
in multiple application areas with high acceptability,
collectability and universality [1] [2]. After the first automatic
face recognition algorithms emerged more than four decades
ago [3], this field has attracted much research and witnesses
incredible progress, with a very large number of face
recognition solutions being used in multiple application areas.
Face recognition technology is assuming an increasingly
important role in our everyday life, what also brings ethical
and privacy dilemmas about how the captured facial
information and the corresponding identity, as a special
category of personal data, should be used, stored and shared.
According to [4], “taxonomy is the practice and
science of classification of things or concepts, including the
principles that underlie such classification”. The availability
of a taxonomy in a certain field allows to
organize/classify/abstract the ‘things’ (in this case, the face
recognition solutions) with two main benefits: i) regarding
the present, it makes it easier to discuss and analyse the
‘things’ and abstract the deeper relations between them, thus
providing a deeper knowledge and comprehension of the full
landscape, notably in terms of strengths and weaknesses;
ii) regarding the future, it makes it easier to understand the
most promising research directions and their implications as
the ‘things’ (in this case, the face recognition solutions) will
not be isolated ‘things’ but rather ‘things’ in a taxonomical
network, inheriting features, strengths and weaknesses from
their taxonomy parents and peers.
Compiling a comprehensive survey of the available
face recognition solutions is a challenging task, notably given
the large number and diversity of solutions developed in the
last decades. To help understanding the structure and
abstraction levels that may be considered in face recognition
solutions, a number of face recognition taxonomies have been
proposed so far [5] [6] [7] [8] [9] [10] [11] [12] [13] [14].
Since the available face recognition taxonomies ignore some
relevant levels of abstraction, which may be helpful for a
more complete characterization of the face recognition
landscape, this paper proposes a new, more encompassing
and richer face recognition multi-level taxonomy.
The new taxonomy can be used to better understand
the technological landscape in the area, facilitating the
characterization and organization of available solutions, and
guiding researchers in the development of more efficient face
recognition solutions for given applications.
The proposed
multi-level taxonomy considers four levels, notably face
structure, feature support, feature extraction approach, and
feature extraction sub-approach. This paper also surveys
representative state-of-the-art face recognition solutions
according to the proposed multi-level taxonomy and
discusses the current algorithmic and application related
challenges and future research directions for face recognition
systems.
The rest of the paper is organized as follows. Section
2 reviews the available face recognition taxonomies, to
understand their benefits and limitations. Section 3 proposes
a new, more encompassing and richer multi-level taxonomy
for face recognition solutions. Section 4 surveys the state-of-
the-art on face recognition under the umbrella of the proposed
multi-level face recognition taxonomy and discusses the
evolutional trends of face recognition over time. Finally,
Section 5 discusses some face recognition challenges and
identifies some relevant future research directions.
2. Reviewing existing face recognition taxonomies
Several face recognition taxonomies have been
proposed in the literature [5] [6] [7] [8] [9] [10] [11] [12] [13]
[14], as summarized in Table 1. This table includes
information about the
abstraction level(s) considered as well
as the corresponding classes – notice that some taxonomies
may use a different terminology. Excluding the taxonomy
proposed in [10], all the other taxonomies listed in Table 1
have been developed based on a single abstraction level to
organize face recognition solutions, thus proposing a specific
taxonomical point of view.
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