FHOG öznitelikleri ile yüz ifadesi tanima

Caner Gacav; Burak Benligiray; Kemal Ozkan; Cihan Topal

doi:10.1109/SIU.2018.8404811

FHOG öznitelikleri ile yüz ifadesi tanima

Caner Gacav
, Burak Benligiray
, Kemal Ozkan
, Cihan Topal

ETGB Teknoparki
Anadolu University
Osmangazi University

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

2 Atıf (Scopus)

Özet

Facial expression recognition is a popular computer vision subject that has many applications such as humancomputer interaction and behavior analysis. As for many computer vision problems, lighting and contrast differences increase the difficulty of the problem. Especially the non-planar structure of the face, protruding regions such as the nose and chin and recessed regions such as eye sockets cause variations in lighting. Another problem with facial expression recognition problems is that the multi-scale detection methods do not align the faces accurately. This leads to comparing features that are extracted from different facial regions, which degrades performance. FHOG features are a contrast-sensitive variation of histogram oriented gradients (HOG) features, which perform well at object detection applications. In this study, the performance of FHOG features at facial expression recognition is investigated. Additionally, aligning with respect to the facial landmarks is proposed to prevent performance degradation due to misalignment. The proposed method is shown to deliver 93% accuracy in facial expression recognition in the extended Cohn-Kanade dataset.

Tercüme edilen katkı başlığı	Facial expression recognition with FHOG features
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1-4
Sayfa sayısı	4
ISBN (Elektronik)	9781538615010
DOI'lar	https://doi.org/10.1109/SIU.2018.8404811
Yayın durumu	Yayınlandı - 5 Tem 2018
Harici olarak yayınlandı	Evet
Etkinlik	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Türkiye Süre: 2 May 2018 → 5 May 2018

Yayın serisi

Adı	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	26th IEEE Signal Processing and Communications Applications Conference, SIU 2018
Ülke/Bölge	Türkiye
Şehir	Izmir
Periyot	2/05/18 → 5/05/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

Keywords

Extended Cohn-Kanade dataset
Facial expression recognition
Felzenszwalb's histogram of oriented gradients
Support vector machines

Belgeye Erişim

10.1109/SIU.2018.8404811

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Gacav, C., Benligiray, B., Ozkan, K., & Topal, C. (2018). FHOG öznitelikleri ile yüz ifadesi tanima. In 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 (pp. 1-4). (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2018.8404811

@inproceedings{090a8fd16dfd4cabaeac67fef6b4a02a,

title = "FHOG {\"o}znitelikleri ile y{\"u}z ifadesi tanima",

abstract = "Facial expression recognition is a popular computer vision subject that has many applications such as humancomputer interaction and behavior analysis. As for many computer vision problems, lighting and contrast differences increase the difficulty of the problem. Especially the non-planar structure of the face, protruding regions such as the nose and chin and recessed regions such as eye sockets cause variations in lighting. Another problem with facial expression recognition problems is that the multi-scale detection methods do not align the faces accurately. This leads to comparing features that are extracted from different facial regions, which degrades performance. FHOG features are a contrast-sensitive variation of histogram oriented gradients (HOG) features, which perform well at object detection applications. In this study, the performance of FHOG features at facial expression recognition is investigated. Additionally, aligning with respect to the facial landmarks is proposed to prevent performance degradation due to misalignment. The proposed method is shown to deliver 93\% accuracy in facial expression recognition in the extended Cohn-Kanade dataset.",

keywords = "Extended Cohn-Kanade dataset, Facial expression recognition, Felzenszwalb's histogram of oriented gradients, Support vector machines",

author = "Caner Gacav and Burak Benligiray and Kemal Ozkan and Cihan Topal",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 ; Conference date: 02-05-2018 Through 05-05-2018",

year = "2018",

month = jul,

day = "5",

doi = "10.1109/SIU.2018.8404811",

language = "T{\"u}rk{\c c}e",

series = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--4",

booktitle = "26th IEEE Signal Processing and Communications Applications Conference, SIU 2018",

address = "United States",

}

Gacav, C, Benligiray, B, Ozkan, K & Topal, C 2018, FHOG öznitelikleri ile yüz ifadesi tanima. in 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, Izmir, Türkiye, 2/05/18. https://doi.org/10.1109/SIU.2018.8404811

FHOG öznitelikleri ile yüz ifadesi tanima. / Gacav, Caner; Benligiray, Burak; Ozkan, Kemal et al.
26th IEEE Signal Processing and Communications Applications Conference, SIU 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4 (26th IEEE Signal Processing and Communications Applications Conference, SIU 2018).

Araştırma çıktısı: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › Hakem

TY - GEN

T1 - FHOG öznitelikleri ile yüz ifadesi tanima

AU - Gacav, Caner

AU - Benligiray, Burak

AU - Ozkan, Kemal

AU - Topal, Cihan

PY - 2018/7/5

Y1 - 2018/7/5

N2 - Facial expression recognition is a popular computer vision subject that has many applications such as humancomputer interaction and behavior analysis. As for many computer vision problems, lighting and contrast differences increase the difficulty of the problem. Especially the non-planar structure of the face, protruding regions such as the nose and chin and recessed regions such as eye sockets cause variations in lighting. Another problem with facial expression recognition problems is that the multi-scale detection methods do not align the faces accurately. This leads to comparing features that are extracted from different facial regions, which degrades performance. FHOG features are a contrast-sensitive variation of histogram oriented gradients (HOG) features, which perform well at object detection applications. In this study, the performance of FHOG features at facial expression recognition is investigated. Additionally, aligning with respect to the facial landmarks is proposed to prevent performance degradation due to misalignment. The proposed method is shown to deliver 93% accuracy in facial expression recognition in the extended Cohn-Kanade dataset.

AB - Facial expression recognition is a popular computer vision subject that has many applications such as humancomputer interaction and behavior analysis. As for many computer vision problems, lighting and contrast differences increase the difficulty of the problem. Especially the non-planar structure of the face, protruding regions such as the nose and chin and recessed regions such as eye sockets cause variations in lighting. Another problem with facial expression recognition problems is that the multi-scale detection methods do not align the faces accurately. This leads to comparing features that are extracted from different facial regions, which degrades performance. FHOG features are a contrast-sensitive variation of histogram oriented gradients (HOG) features, which perform well at object detection applications. In this study, the performance of FHOG features at facial expression recognition is investigated. Additionally, aligning with respect to the facial landmarks is proposed to prevent performance degradation due to misalignment. The proposed method is shown to deliver 93% accuracy in facial expression recognition in the extended Cohn-Kanade dataset.

KW - Extended Cohn-Kanade dataset

KW - Facial expression recognition

KW - Felzenszwalb's histogram of oriented gradients

KW - Support vector machines

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DO - 10.1109/SIU.2018.8404811

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018

Y2 - 2 May 2018 through 5 May 2018

ER -

FHOG öznitelikleri ile yüz ifadesi tanima

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap