Face Recognition via Taxonomy of Illumination Normalization

Karamizadeh, Sasan; Abdullah, Shahidan M.; Zamani, Mazdak; Shayan, Jafar; Nooralishahi, Parham

doi:10.1007/978-3-319-44270-9_7

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…Several face recognition taxonomies have been proposed in the literature [5][6][7][8][9][10][11][12][13][14], as summarised 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 different terminology.…”

Section: Reviewing Existing Face Recognition Taxonomiesmentioning

confidence: 99%

“…Two recent face recognition taxonomies organize the face recognition solutions depending on the specific conditions addressed, such as illumination [13] and pose [14] dependencies, not being encompassing enough to consider every face recognition solution. Other taxonomies organize the face recognition solutions based on the sensing modality [7] [12], or on the adopted modality matching, to consider the cases where the enrolment and test data are captured using different imaging modalities [8].…”

Section: Reviewing Existing Face Recognition Taxonomiesmentioning

confidence: 99%

“…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 to understand 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 complete characterisation of the face recognition landscape, this paper proposes a new, more encompassing and richer face recognition multi-level taxonomy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Face recognition: a novel multi‐level taxonomy based survey

2019

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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.

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Section: Reviewing Existing Face Recognition Taxonomiesmentioning

confidence: 99%

Section: Reviewing Existing Face Recognition Taxonomiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Face recognition: a novel multi‐level taxonomy based survey

2019

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“…Approaches include normalization (face images are transformed to same scale), face alignment (defined by (Jin & Tan, 2017) as locating fiducial points on face image) and enhancement of image (stated by (Karamizadeh et al, 2016) as processing the face image into an enhanced version which has the potential to enhance face recognition system performance).…”

Section: Introductionmentioning

confidence: 99%

Face Recognition based on Convoluted Neural Networks: Technical Review

Shakkak

MAZRUII

2022

ACJ

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Human beings recognize and classify objects with biological senses and brain that processes the input into meaningful information. Other than that humans have come to recognize each other in multiple ways one of which is visual recognition of faces. As a biological trait human faces are certainly a biometric such they are universal, distinctive, mostly permanent and collectable. With that a computerized face recognition system can constructed relying on visual information present on each face uniquely. Generally a face recognition system consists of two main phases, face detection phase where presence of a human face is verified on visual input and face recognition phase where detected face is processed for identification. One of the most sought after methods in field image processing for face recognition is CNN (Convoluted Neural Networks). CNNs have proved its effectiveness and accuracy in many CNN based face detection and face recognition systems. As such in this paper the architecture of CNN is presented. Then different techniques for face detection and face recognition based on CNNs are reviewed. In reviewed papers CNNs have repeatedly demonstrated effectiveness and accuracy on multiple benchmarks for face recognition application.

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Analysis of Factors Improving Accuracy of Passive User Identification with Streams of Face Images for Ubiquitous Commerce

Wójtowicz

Chmielewski

2020

HCI for Cybersecurity, Privacy and Trust

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Face Recognition via Taxonomy of Illumination Normalization

Cited by 8 publications

References 41 publications

Face recognition: a novel multi‐level taxonomy based survey

Face recognition: a novel multi‐level taxonomy based survey

Face Recognition based on Convoluted Neural Networks: Technical Review

Analysis of Factors Improving Accuracy of Passive User Identification with Streams of Face Images for Ubiquitous Commerce

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