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A Novel Approach for Facial Attendance:AttendXNet

Aptisi Transactions on Technopreneurship (ATT) · Vol. 2 No. 2 (2020) · hlm. 104–111

DOI
10.34306/att.v2i2.86
ISSN
2656-8888, 2655-8807
Terbit
2020-06-05
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Recent advancements in the area of facial recognition and verification introduced the possibility of facial attendance for various use cases. In this paper we present a system named as AttendXNet. Our method uses the ResNet and Multi-layer feed forward network to achieve the state of art results. Extensive analysis of various deep learning and machine learning techniques is described. Face anti-spoofing is a major challenge in facial attendance. Extended-MobileNet is used to resolve the same issue. We also introduced the end to end pipeline to implement an attendance system for various use cases.

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33 penulis · 88 tautan kepenulisan bersama · 30 lintas organisasi

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  • Universitas Raharja
  • Graphic Era Hill University
  • IFTM University
  • International Islamic University Malaysia
  • Universitas Esa Unggul
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Bhupesh RawatGraphic Era Hill University3
Kawal AroraAfiliasi tidak diketahui3
Saksham ChaurasiaAfiliasi tidak diketahui2
Srishti BiswasAfiliasi tidak diketahui1
Ritesh MishraIFTM University1
Marviola HardiniAfiliasi tidak diketahui1
Chitra PandeyGraphic Era Hill University1
Marviola Grace HardiniUniversitas Raharja1
Megha UpretiGraphic Era Hill University1
Buphest RawatGraphic Era Hill University1
Mukti BudiartoUniversitas Raharja1
Ari AsmawatiUniversitas Raharja1
Prakriti RashiGraphic Era Hill University1
Wahyu Yustika PrihastiwiUniversitas Raharja1
Siti Nurindah SariUniversitas Raharja1
Dedeh SupriyantiUniversitas Raharja1
Vonda ElmandaUniversitas Raharja1
MulyatiAfiliasi tidak diketahui1
Muhammad FakhrezzyUniversitas Raharja1
Regina Dinda OctavyraUniversitas Raharja1
Ankur Singh BistAfiliasi tidak diketahui1
Desy AprianiUniversitas Raharja1
Efa Ayu NabilaUniversitas Raharja1
Nur Ihsan PermadiUniversitas Raharja1
Greisy JacquelineAfiliasi tidak diketahui1
Nizirwan AnwarUniversitas Esa Unggul1
Noor Azura ZakariaInternational Islamic University Malaysia1
Greisy JacquelineAfiliasi tidak diketahui1
Ming LiAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://att.aptisi.or.id/index.php/att/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">A Novel Approach for Facial Attendance:AttendXNet</dc:title>
	<dc:creator xml:lang="en">Arora, Kawal</dc:creator>
	<dc:creator xml:lang="en">Bist, Ankur Singh</dc:creator>
	<dc:creator xml:lang="en">Prakash, Roshan</dc:creator>
	<dc:creator xml:lang="en">Chaurasia, Saksham</dc:creator>
	<dc:subject xml:lang="en">Feature extraction, Support Vector Machine, Muli-layer Neural Network, Face Anti-spoofing, Faiss</dc:subject>
	<dc:description xml:lang="en">Recent advancements in the area of facial recognition and verification introduced the possibility of facial attendance for various use cases. In this paper we present a system named as AttendXNet. Our method uses the ResNet and Multi-layer feed forward network to achieve the state of art results. Extensive analysis of various deep learning and machine learning techniques is described. Face anti-spoofing is a major challenge in facial attendance. Extended-MobileNet is used to resolve the same issue. We also introduced the end to end pipeline to implement an attendance system for various use cases.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2020-06-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:format>application/epub+zip</dc:format>
	<dc:identifier>https://att.aptisi.or.id/index.php/att/article/view/86</dc:identifier>
	<dc:identifier>10.34306/att.v2i2.86</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 2 No. 2 (2020): September; 104 - 111</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 2 No 2 (2020): September; 104 - 111</dc:source>
	<dc:source>2656-8888</dc:source>
	<dc:source>2655-8807</dc:source>
	<dc:source>10.34306/att.v2i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/86/33</dc:relation>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/86/34</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2020 Kawal Arora, Ankur Singh Bist, Roshan Prakash, Saksham Chaurasia</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>