AUTOMATIC CLASS ATTENDANCE MANAGEMENT SYSTEM USING FACIAL SYNTHESIS AND RECOGNITION / (Kayıt no. 288999)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03274nam a22002777a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20221104110125.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 221104d2022 cy ||||| m||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | D 330 |
| Cutter no | M45 2022 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Milad, Ali Ali Alhadar |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | AUTOMATIC CLASS ATTENDANCE MANAGEMENT SYSTEM USING FACIAL SYNTHESIS AND RECOGNITION / |
| Sorumluluk Bildirimi | ALI ASLI ALHADAR MILAD; SUPERVISOR: ASSOC. PROF. DR. KAMIL YURTKAN |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 135 sheets; |
| Boyutları | 31 cm. |
| Birlikteki Materyal | Includes CD |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (PhD) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department |
| 504 ## - BİBLİYOGRAFİ NOTU | |
| Bibliyografi Notu | Includes bibliography (sheets 124-135) |
| 520 ## - ÖZET NOTU | |
| Özet notu | ABSTRACT<br/>Classroom attendance systems have been a principal part of the learning process of<br/>conventional classroom institutions. Many processes have been devised over the<br/>years for the adequate recording of student attendance in classrooms to ensure<br/>students attend their classes which is synonymous to good academic performance in<br/>students. The rise of the digital age in the 21st century has enabled the introduction<br/>of several electronic technological platforms for classroom attendance purposes, one<br/>of such technologies is the use of human face recognition for classroom attendance.<br/>Unfortunately classroom attendance has been faced by several challenges which<br/>many studies have tried to mitigate and proffer solutions to. This study proposed a<br/>methodology to mitigate the challenges of varying face poses and facial expressions<br/>which both face recognition-based classroom attendance systems have been reported<br/>to face over the years. This research leverages the use of 3D analysis of facial<br/>expressions to synthesize face image poses and facial expressions from a single input<br/>image of a subject. The study uses six basic facial expressions which it implements<br/>using a 3D model of set of points triangle mesh points connected in 3D space of 20<br/>geometric feature points on human faces. The synthesized facial images are applied<br/>Local Binary Pattern Histogram (LBPH) texture operator on, to enable the extraction<br/>of histograms which are used to train a Support Vector Machine (SVM) classifier.<br/>The study uses two different human face databases; FEI and BU-3DFE, the two<br/>databases are used to evaluate the proposed methodology separately, with the FEI<br/>database images yielding an average of 71.78% performance accuracy and the BU-<br/>3DFE database yielding an average of 78.57% performance accuracy. This designed<br/>methodology is further developed and applied as a recognition system for classroom<br/>attendance scenarios which led to the achievement of a satisfactory performance<br/>accuracy for individual subjects tested on it.<br/>Keywords: Classroom Attendance System, 3D Face Analysis, Face Recognition,<br/>Support Vector Machine, Local Binary Pattern Histogram |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Human face recognition (Computer science) |
| Alt başlık biçimi | Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Support vector machines |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Yurtkan, Kamil |
| İlişkili Terim | supervisor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Tez Koleksiyonu | 04.11.2022 | Bağış | D 330 M45 2022 | T2909 | 04.11.2022 | 04.11.2022 | Thesis | Management Information Systems Department | |||
| Dewey Onlu Sınıflama Sistemi | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 04.11.2022 | Bağış | D 330 M45 2022 | CDT2909 | 04.11.2022 | 04.11.2022 | Suppl. CD | Management Information Systems Department |