Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed

Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741

Browse

Filters

2014 - 20172

Settings

Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperHas files: NoSubject: search.filters.subject.Signal processingAuthor: search.filters.author.Arica, NafizAuthor: search.filters.author.Çelik, Anıl

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    PublicationMetadata only
    An hierarchical approach for human computer interaction using eyelid movements, Göz kapaǧi hareketleriyle insan bilgisayar etkileşimi için siradüzensel yaklaşim
    (IEEE Computer Society [email protected], 2014) Çelik, Anıl; Arica, Nafiz; Çelik, Anıl, Bahçeşehir Üniversitesi, Istanbul, Turkey; Arica, Nafiz, Bahçeçehir Üniversitesi, Istanbul, Turkey
    This work proposes a method to achieve real-time HumanComputer interaction with the movements of the eyelids in low-resolution video. Classification of left and right eye states as closed or open are performed by an hierarchical approach of tracking by detection. After the initial detection of the face area, an efficient face tracking algorithm is used to reduce the search space for detection of eye region. By seperating the eye region into two overlapping pieces, left and right eyes are detected and classified as closed or open. The proposed method is robust against mimics and multiple faces in the frame, while being unaffected by the negative effects of aliasing and resizing. Thus, people who suffer from medical conditions limiting their physical movement capabilities can interact with computers with their eyelid movements. © 2014 IEEE. © 2014 Elsevier B.V., All rights reserved.
  • No Thumbnail Available
    PublicationMetadata only
    Face frontalization enhanced by deep learning, Derin Öǧrenme Destekli Yüz Önleştirme
    (Institute of Electrical and Electronics Engineers Inc., 2017) Çelik, Anıl; Arica, Nafiz; Çelik, Anıl, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Arica, Nafiz, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey
    In this study, a new approach based on 3-D models and deep learning to frontalize face images is proposed. Specifically designed for facial expression analysis, the proposed approach aims to reduce possible negative effects that a posed face image can generate, by normalizing the face region. In the first phase, the face image is semi-frontalized, with a pre-established 3-D reference model based approach. Then, missing regions on semi-frontalized images due to geometric transformation are reconstructed with the help of a denoising stacked autoencoder network. In this phase, missing regions created by line of sight are learned, with a deep architecture, using numerous images. When examined, it can be said that, faces acquired with the proposed approach, are objectively better than the faces acquired with a deep learning or 3-D based method alone. Therefore, it is assumed that the proposed approach can be used in face based computer vision methods as a beneficial pre-processing step. © 2017 Elsevier B.V., All rights reserved.