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Start date: 2010End date: 2019Type: search.filters.itemtype.Conference PaperHas files: NoSubject: search.filters.subject.Signal processingSubject: search.filters.subject.Deep LearningAuthor: search.filters.author.Arica, Nafiz

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    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.
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    Facial emotion classification using deep embedding with triplet loss function, Üçlü Yitim Fonksiyonu Tabanli Derin Gömme ile YÜz Duygu Siniflandirmasi
    (Institute of Electrical and Electronics Engineers Inc., 2017) Bircanoglu, Cenk; Arica, Nafiz; Bircanoglu, Cenk, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Arica, Nafiz, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey
    In this paper, a deep embedding method using triplet loss function is proposed for classification of the emotions in face images. The originality of proposed method lies in the loss function, different from the other deep learning-based facial emotion classification approaches. The input face images are embedded into a lower dimensional feature space using a multi-layer convolutional neural network. In this embedding process the loss function is calculated by taking triple samples in the training dataset. For each sample in batches, two samples are selected in such a way that one of them is from the same class and the other one from the different class. The loss function aims to close the samples belonging to the same class to each other in the Euclidean space and to move away from the samples in the different class. In the performance analysis of proposed method, two popular deep architectures, namely AlexNet and VGG are used on two datasets called CIFE and GaMo. The experiments show that the proposed method outperforms the other popular loss functions and the available studies on this particular datasets. © 2017 Elsevier B.V., All rights reserved.
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    A comparison of activation functions in artificial neural networks, Yapay sinir aǧlarinda aktivasyon fonksiyonlarinin karşilaştirilmasi
    (Institute of Electrical and Electronics Engineers Inc., 2018) Bircanoglu, Cenk; Arica, Nafiz; Bircanoglu, Cenk, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey; Arica, Nafiz, Bilgisayar Mühendisliǧi Bölümü, Bahçeşehir Üniversitesi, Istanbul, Turkey
    In this study, the effects of Activation Functions (AF) in Artificial Neural Network (ANN) on regression and classification performance are compared. In comparisons, success rates in test data and duration of training are evaluated for both problems. A total of 11 AF functions, 10 AF commonly used in the literature and Square function proposed in this study, are compared using 7 different datasets, 2 for regression and 5 for classification. 3 different ANN architectures, which are considered to be the most appropriate for each dataset are employed in the experiments. As a result of totally 231 different training procedures, the effects of Afs are examined for different datasets and architectures. Similarly, the effects of AF on training time are shown for different datasets. In the experiments it is shown that ReLU is the most succesfull AF in general purposes. In addition to ReLU, Square function gives the better results in image datasets. © 2018 Elsevier B.V., All rights reserved.