Design and Implementation of an Asymmetric Face Recognition System Based on Unsupervised Learning Article Swipe
Chih‐Yung Chang
,
Arpita Samanta santra
,
I-Hsiung Chang
,
Shih-Jung Wu
,
Diptendu Sinha Roy
,
Qiaoyun Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2751422/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2751422/v1
This paper mainly discusses the asymmetric face recognition problem where the number of names in a name list and the number of faces in the photo might not be equal, but each face should be automatically labeled with a name. The motivation for this issue is that there had been many meetings in the past. After each meeting, the participant took group photos. The meeting provided only a corresponding name list of participants without one-to-one labels. In the worst case, the group photo might mix with the faces that were not participating in the meeting. Another reason for asymmetric face recognition is that some meeting personnel did not appear in photos because they assisted in taking pictures. This paper proposes an Asymmetric Face Recognition Mechanism, called AFRM in short. Initially, the proposed AFRM adopts the Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) to detect and extract all faces from photos. Next, AFRM extracts the features from each face using the convolution feature map (Conv_FF) and adopts the features to partition the faces into different classes. Then the AFRM applies the statistic-based mechanism to map each name in the name list to each face class. According to this mapping, each face will be associated with one name. To quickly identify a face during the meeting, the AFRM applies the K-Nearest Neighbors ( KNN ) to represent the features of each face. During the new meeting, the proposed AFRM can extract the feature of one face and then adopts KNN to derive the features. Experimental results show that the proposed mechanism achieves more than 97% accuracy without one-to-one name and face labeling.
Related Topics
Concepts
Computer science
Artificial intelligence
Pattern recognition (psychology)
Histogram
Face (sociological concept)
Partition (number theory)
Support vector machine
Facial recognition system
Feature (linguistics)
Histogram of oriented gradients
Image (mathematics)
Mathematics
Linguistics
Philosophy
Combinatorics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2751422/v1
- https://www.researchsquare.com/article/rs-2751422/latest.pdf
- OA Status
- green
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361981888
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4361981888Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-2751422/v1Digital Object Identifier
- Title
-
Design and Implementation of an Asymmetric Face Recognition System Based on Unsupervised LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-31Full publication date if available
- Authors
-
Chih‐Yung Chang, Arpita Samanta santra, I-Hsiung Chang, Shih-Jung Wu, Diptendu Sinha Roy, Qiaoyun ZhangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2751422/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-2751422/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-2751422/latest.pdfDirect OA link when available
- Concepts
-
Computer science, Artificial intelligence, Pattern recognition (psychology), Histogram, Face (sociological concept), Partition (number theory), Support vector machine, Facial recognition system, Feature (linguistics), Histogram of oriented gradients, Image (mathematics), Mathematics, Linguistics, Philosophy, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4361981888 |
|---|---|
| doi | https://doi.org/10.21203/rs.3.rs-2751422/v1 |
| ids.doi | https://doi.org/10.21203/rs.3.rs-2751422/v1 |
| ids.openalex | https://openalex.org/W4361981888 |
| fwci | 0.0 |
| type | preprint |
| title | Design and Implementation of an Asymmetric Face Recognition System Based on Unsupervised Learning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10057 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9987000226974487 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Face and Expression Recognition |
| topics[1].id | https://openalex.org/T11448 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9984999895095825 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Face recognition and analysis |
| topics[2].id | https://openalex.org/T10828 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9904000163078308 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1711 |
| topics[2].subfield.display_name | Signal Processing |
| topics[2].display_name | Biometric Identification and Security |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7049815654754639 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.6649590134620667 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6022644639015198 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| concepts[3].id | https://openalex.org/C53533937 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5965729355812073 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q185020 |
| concepts[3].display_name | Histogram |
| concepts[4].id | https://openalex.org/C2779304628 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5866419672966003 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3503480 |
| concepts[4].display_name | Face (sociological concept) |
| concepts[5].id | https://openalex.org/C42812 |
| concepts[5].level | 2 |
| concepts[5].score | 0.585713803768158 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1082910 |
| concepts[5].display_name | Partition (number theory) |
| concepts[6].id | https://openalex.org/C12267149 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5680561661720276 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[6].display_name | Support vector machine |
| concepts[7].id | https://openalex.org/C31510193 |
| concepts[7].level | 3 |
| concepts[7].score | 0.5532870292663574 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1192553 |
| concepts[7].display_name | Facial recognition system |
| concepts[8].id | https://openalex.org/C2776401178 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5057601928710938 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[8].display_name | Feature (linguistics) |
| concepts[9].id | https://openalex.org/C17426736 |
| concepts[9].level | 4 |
| concepts[9].score | 0.43903791904449463 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q419918 |
| concepts[9].display_name | Histogram of oriented gradients |
| concepts[10].id | https://openalex.org/C115961682 |
| concepts[10].level | 2 |
| concepts[10].score | 0.2734616696834564 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[10].display_name | Image (mathematics) |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.1687852442264557 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C41895202 |
| concepts[12].level | 1 |
| concepts[12].score | 0.07799354195594788 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[12].display_name | Linguistics |
| concepts[13].id | https://openalex.org/C138885662 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[13].display_name | Philosophy |
| concepts[14].id | https://openalex.org/C114614502 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q76592 |
| concepts[14].display_name | Combinatorics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7049815654754639 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.6649590134620667 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.6022644639015198 |
| keywords[2].display_name | Pattern recognition (psychology) |
| keywords[3].id | https://openalex.org/keywords/histogram |
| keywords[3].score | 0.5965729355812073 |
| keywords[3].display_name | Histogram |
| keywords[4].id | https://openalex.org/keywords/face |
| keywords[4].score | 0.5866419672966003 |
| keywords[4].display_name | Face (sociological concept) |
| keywords[5].id | https://openalex.org/keywords/partition |
| keywords[5].score | 0.585713803768158 |
| keywords[5].display_name | Partition (number theory) |
| keywords[6].id | https://openalex.org/keywords/support-vector-machine |
| keywords[6].score | 0.5680561661720276 |
| keywords[6].display_name | Support vector machine |
| keywords[7].id | https://openalex.org/keywords/facial-recognition-system |
| keywords[7].score | 0.5532870292663574 |
| keywords[7].display_name | Facial recognition system |
| keywords[8].id | https://openalex.org/keywords/feature |
| keywords[8].score | 0.5057601928710938 |
| keywords[8].display_name | Feature (linguistics) |
| keywords[9].id | https://openalex.org/keywords/histogram-of-oriented-gradients |
| keywords[9].score | 0.43903791904449463 |
| keywords[9].display_name | Histogram of oriented gradients |
| keywords[10].id | https://openalex.org/keywords/image |
| keywords[10].score | 0.2734616696834564 |
| keywords[10].display_name | Image (mathematics) |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.1687852442264557 |
| keywords[11].display_name | Mathematics |
| keywords[12].id | https://openalex.org/keywords/linguistics |
| keywords[12].score | 0.07799354195594788 |
| keywords[12].display_name | Linguistics |
| language | en |
| locations[0].id | doi:10.21203/rs.3.rs-2751422/v1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402450 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Research Square (Research Square) |
| locations[0].source.host_organization | https://openalex.org/I4210096694 |
| locations[0].source.host_organization_name | Research Square (United States) |
| locations[0].source.host_organization_lineage | https://openalex.org/I4210096694 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.researchsquare.com/article/rs-2751422/latest.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21203/rs.3.rs-2751422/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5002582301 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0672-5593 |
| authorships[0].author.display_name | Chih‐Yung Chang |
| authorships[0].countries | TW |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I107470533 |
| authorships[0].affiliations[0].raw_affiliation_string | Tamkang University, Taiwan; |
| authorships[0].institutions[0].id | https://openalex.org/I107470533 |
| authorships[0].institutions[0].ror | https://ror.org/04tft4718 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I107470533 |
| authorships[0].institutions[0].country_code | TW |
| authorships[0].institutions[0].display_name | Tamkang University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chih-Yung Chang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Tamkang University, Taiwan; |
| authorships[1].author.id | https://openalex.org/A5010935606 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Arpita Samanta santra |
| authorships[1].countries | TW |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I25846049 |
| authorships[1].affiliations[0].raw_affiliation_string | National Tsing Hua University, Taiwan; |
| authorships[1].institutions[0].id | https://openalex.org/I25846049 |
| authorships[1].institutions[0].ror | https://ror.org/00zdnkx70 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I25846049 |
| authorships[1].institutions[0].country_code | TW |
| authorships[1].institutions[0].display_name | National Tsing Hua University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Arpita Samanta santra |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | National Tsing Hua University, Taiwan; |
| authorships[2].author.id | https://openalex.org/A5066138411 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | I-Hsiung Chang |
| authorships[2].countries | TW |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I134161618 |
| authorships[2].affiliations[0].raw_affiliation_string | National Taiwan Normal University, Taiwan; |
| authorships[2].institutions[0].id | https://openalex.org/I134161618 |
| authorships[2].institutions[0].ror | https://ror.org/059dkdx38 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I134161618 |
| authorships[2].institutions[0].country_code | TW |
| authorships[2].institutions[0].display_name | National Taiwan Normal University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | I-Hsiung Chang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | National Taiwan Normal University, Taiwan; |
| authorships[3].author.id | https://openalex.org/A5047149192 |
| authorships[3].author.orcid | https://orcid.org/0009-0006-2320-2985 |
| authorships[3].author.display_name | Shih-Jung Wu |
| authorships[3].countries | TW |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I107470533 |
| authorships[3].affiliations[0].raw_affiliation_string | Tamkang University, Taiwan; |
| authorships[3].institutions[0].id | https://openalex.org/I107470533 |
| authorships[3].institutions[0].ror | https://ror.org/04tft4718 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I107470533 |
| authorships[3].institutions[0].country_code | TW |
| authorships[3].institutions[0].display_name | Tamkang University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Shih-Jung Wu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Tamkang University, Taiwan; |
| authorships[4].author.id | https://openalex.org/A5011943818 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-9731-2534 |
| authorships[4].author.display_name | Diptendu Sinha Roy |
| authorships[4].countries | IN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I44635919 |
| authorships[4].affiliations[0].raw_affiliation_string | National Institute of Technology, India; |
| authorships[4].institutions[0].id | https://openalex.org/I44635919 |
| authorships[4].institutions[0].ror | https://ror.org/032twef21 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210152752, https://openalex.org/I44635919 |
| authorships[4].institutions[0].country_code | IN |
| authorships[4].institutions[0].display_name | National Institute of Technology Delhi |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Diptendu Sinha Roy |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | National Institute of Technology, India; |
| authorships[5].author.id | https://openalex.org/A5063370676 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-8072-5973 |
| authorships[5].author.display_name | Qiaoyun Zhang |
| authorships[5].countries | CN, TW |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I107470533 |
| authorships[5].affiliations[0].raw_affiliation_string | Tamkang University, Taiwan; |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I4210115456 |
| authorships[5].affiliations[1].raw_affiliation_string | Chuzhou University, China |
| authorships[5].institutions[0].id | https://openalex.org/I4210115456 |
| authorships[5].institutions[0].ror | https://ror.org/037663q52 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210115456 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Chuzhou University |
| authorships[5].institutions[1].id | https://openalex.org/I107470533 |
| authorships[5].institutions[1].ror | https://ror.org/04tft4718 |
| authorships[5].institutions[1].type | education |
| authorships[5].institutions[1].lineage | https://openalex.org/I107470533 |
| authorships[5].institutions[1].country_code | TW |
| authorships[5].institutions[1].display_name | Tamkang University |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Qiaoyun Zhang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Chuzhou University, China, Tamkang University, Taiwan; |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.researchsquare.com/article/rs-2751422/latest.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Design and Implementation of an Asymmetric Face Recognition System Based on Unsupervised Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10057 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9987000226974487 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Face and Expression Recognition |
| related_works | https://openalex.org/W2562641072, https://openalex.org/W2071599417, https://openalex.org/W2048716406, https://openalex.org/W1870444468, https://openalex.org/W1964725559, https://openalex.org/W3109748140, https://openalex.org/W2045053268, https://openalex.org/W2433492094, https://openalex.org/W2767833206, https://openalex.org/W1556327589 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21203/rs.3.rs-2751422/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402450 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Research Square (Research Square) |
| best_oa_location.source.host_organization | https://openalex.org/I4210096694 |
| best_oa_location.source.host_organization_name | Research Square (United States) |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I4210096694 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.researchsquare.com/article/rs-2751422/latest.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-2751422/v1 |
| primary_location.id | doi:10.21203/rs.3.rs-2751422/v1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402450 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Research Square (Research Square) |
| primary_location.source.host_organization | https://openalex.org/I4210096694 |
| primary_location.source.host_organization_name | Research Square (United States) |
| primary_location.source.host_organization_lineage | https://openalex.org/I4210096694 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.researchsquare.com/article/rs-2751422/latest.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-2751422/v1 |
| publication_date | 2023-03-31 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2920889198, https://openalex.org/W6835704836, https://openalex.org/W6713185947, https://openalex.org/W2495996051, https://openalex.org/W2208564731, https://openalex.org/W2803386491, https://openalex.org/W3033123268, https://openalex.org/W3197287897, https://openalex.org/W2386870756, https://openalex.org/W6728374919, https://openalex.org/W3204032804, https://openalex.org/W3206484473, https://openalex.org/W3198276219, https://openalex.org/W3134999934, https://openalex.org/W3034140121, https://openalex.org/W2904427185, https://openalex.org/W2618353479, https://openalex.org/W3015703757, https://openalex.org/W3019524780, https://openalex.org/W3017068813, https://openalex.org/W2921036759, https://openalex.org/W2712273292, https://openalex.org/W2910976212, https://openalex.org/W6811155931, https://openalex.org/W2531440880, https://openalex.org/W2963975998 |
| referenced_works_count | 26 |
| abstract_inverted_index.( | 224 |
| abstract_inverted_index.) | 226 |
| abstract_inverted_index.a | 16, 39, 68, 213 |
| abstract_inverted_index.In | 77 |
| abstract_inverted_index.To | 210 |
| abstract_inverted_index.an | 121 |
| abstract_inverted_index.be | 29, 35, 205 |
| abstract_inverted_index.in | 15, 24, 53, 93, 110, 115, 128, 190 |
| abstract_inverted_index.is | 46, 102 |
| abstract_inverted_index.of | 13, 22, 72, 137, 231, 245 |
| abstract_inverted_index.to | 146, 172, 186, 194, 199, 227, 252 |
| abstract_inverted_index.97% | 266 |
| abstract_inverted_index.KNN | 225, 251 |
| abstract_inverted_index.The | 41, 64 |
| abstract_inverted_index.all | 150 |
| abstract_inverted_index.and | 19, 141, 148, 168, 248, 271 |
| abstract_inverted_index.but | 31 |
| abstract_inverted_index.can | 241 |
| abstract_inverted_index.did | 107 |
| abstract_inverted_index.for | 43, 98 |
| abstract_inverted_index.had | 49 |
| abstract_inverted_index.map | 166, 187 |
| abstract_inverted_index.mix | 85 |
| abstract_inverted_index.new | 236 |
| abstract_inverted_index.not | 28, 91, 108 |
| abstract_inverted_index.one | 208, 246 |
| abstract_inverted_index.the | 5, 11, 20, 25, 54, 59, 78, 81, 87, 94, 131, 135, 157, 163, 170, 174, 180, 183, 191, 216, 218, 221, 229, 235, 238, 243, 254, 260 |
| abstract_inverted_index.AFRM | 127, 133, 155, 181, 219, 240 |
| abstract_inverted_index.Face | 123 |
| abstract_inverted_index.Then | 179 |
| abstract_inverted_index.This | 1, 118 |
| abstract_inverted_index.been | 50 |
| abstract_inverted_index.each | 32, 57, 160, 188, 195, 202, 232 |
| abstract_inverted_index.face | 7, 33, 100, 161, 196, 203, 214, 247, 272 |
| abstract_inverted_index.from | 152, 159 |
| abstract_inverted_index.into | 176 |
| abstract_inverted_index.list | 18, 71, 193 |
| abstract_inverted_index.many | 51 |
| abstract_inverted_index.more | 264 |
| abstract_inverted_index.name | 17, 70, 189, 192, 270 |
| abstract_inverted_index.only | 67 |
| abstract_inverted_index.show | 258 |
| abstract_inverted_index.some | 104 |
| abstract_inverted_index.than | 265 |
| abstract_inverted_index.that | 47, 89, 103, 259 |
| abstract_inverted_index.then | 249 |
| abstract_inverted_index.they | 113 |
| abstract_inverted_index.this | 44, 200 |
| abstract_inverted_index.took | 61 |
| abstract_inverted_index.were | 90 |
| abstract_inverted_index.will | 204 |
| abstract_inverted_index.with | 38, 86, 207 |
| abstract_inverted_index.(HOG) | 140 |
| abstract_inverted_index.(SVM) | 145 |
| abstract_inverted_index.After | 56 |
| abstract_inverted_index.Next, | 154 |
| abstract_inverted_index.case, | 80 |
| abstract_inverted_index.face. | 233 |
| abstract_inverted_index.faces | 23, 88, 151, 175 |
| abstract_inverted_index.group | 62, 82 |
| abstract_inverted_index.issue | 45 |
| abstract_inverted_index.might | 27, 84 |
| abstract_inverted_index.name. | 40, 209 |
| abstract_inverted_index.names | 14 |
| abstract_inverted_index.paper | 2, 119 |
| abstract_inverted_index.past. | 55 |
| abstract_inverted_index.photo | 26, 83 |
| abstract_inverted_index.there | 48 |
| abstract_inverted_index.using | 162 |
| abstract_inverted_index.where | 10 |
| abstract_inverted_index.worst | 79 |
| abstract_inverted_index.During | 234 |
| abstract_inverted_index.Vector | 143 |
| abstract_inverted_index.adopts | 134, 169, 250 |
| abstract_inverted_index.appear | 109 |
| abstract_inverted_index.called | 126 |
| abstract_inverted_index.class. | 197 |
| abstract_inverted_index.derive | 253 |
| abstract_inverted_index.detect | 147 |
| abstract_inverted_index.during | 215 |
| abstract_inverted_index.equal, | 30 |
| abstract_inverted_index.mainly | 3 |
| abstract_inverted_index.number | 12, 21 |
| abstract_inverted_index.photos | 111 |
| abstract_inverted_index.reason | 97 |
| abstract_inverted_index.short. | 129 |
| abstract_inverted_index.should | 34 |
| abstract_inverted_index.taking | 116 |
| abstract_inverted_index.Another | 96 |
| abstract_inverted_index.Machine | 144 |
| abstract_inverted_index.Support | 142 |
| abstract_inverted_index.applies | 182, 220 |
| abstract_inverted_index.because | 112 |
| abstract_inverted_index.extract | 149, 242 |
| abstract_inverted_index.feature | 165, 244 |
| abstract_inverted_index.labeled | 37 |
| abstract_inverted_index.labels. | 76 |
| abstract_inverted_index.meeting | 65, 105 |
| abstract_inverted_index.photos. | 63, 153 |
| abstract_inverted_index.problem | 9 |
| abstract_inverted_index.quickly | 211 |
| abstract_inverted_index.results | 257 |
| abstract_inverted_index.without | 74, 268 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Oriented | 138 |
| abstract_inverted_index.accuracy | 267 |
| abstract_inverted_index.achieves | 263 |
| abstract_inverted_index.assisted | 114 |
| abstract_inverted_index.classes. | 178 |
| abstract_inverted_index.extracts | 156 |
| abstract_inverted_index.features | 158, 171, 230 |
| abstract_inverted_index.identify | 212 |
| abstract_inverted_index.mapping, | 201 |
| abstract_inverted_index.meeting, | 58, 217, 237 |
| abstract_inverted_index.meeting. | 95 |
| abstract_inverted_index.meetings | 52 |
| abstract_inverted_index.proposed | 132, 239, 261 |
| abstract_inverted_index.proposes | 120 |
| abstract_inverted_index.provided | 66 |
| abstract_inverted_index.(Conv_FF) | 167 |
| abstract_inverted_index.According | 198 |
| abstract_inverted_index.Gradients | 139 |
| abstract_inverted_index.Histogram | 136 |
| abstract_inverted_index.K-Nearest | 222 |
| abstract_inverted_index.Neighbors | 223 |
| abstract_inverted_index.different | 177 |
| abstract_inverted_index.discusses | 4 |
| abstract_inverted_index.features. | 255 |
| abstract_inverted_index.labeling. | 273 |
| abstract_inverted_index.mechanism | 185, 262 |
| abstract_inverted_index.partition | 173 |
| abstract_inverted_index.personnel | 106 |
| abstract_inverted_index.pictures. | 117 |
| abstract_inverted_index.represent | 228 |
| abstract_inverted_index.Asymmetric | 122 |
| abstract_inverted_index.Initially, | 130 |
| abstract_inverted_index.Mechanism, | 125 |
| abstract_inverted_index.associated | 206 |
| abstract_inverted_index.asymmetric | 6, 99 |
| abstract_inverted_index.motivation | 42 |
| abstract_inverted_index.one-to-one | 75, 269 |
| abstract_inverted_index.Recognition | 124 |
| abstract_inverted_index.convolution | 164 |
| abstract_inverted_index.participant | 60 |
| abstract_inverted_index.recognition | 8, 101 |
| abstract_inverted_index.Experimental | 256 |
| abstract_inverted_index.participants | 73 |
| abstract_inverted_index.automatically | 36 |
| abstract_inverted_index.corresponding | 69 |
| abstract_inverted_index.participating | 92 |
| abstract_inverted_index.statistic-based | 184 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.02903602 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |