Yazar "Soylemez, Omer Faruk" seçeneğine göre listele

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    Circular Hough Transform based Eye State Detection In Human Face Images
    (Ieee, 2013) Soylemez, Omer Faruk; Ergen, Burhan
    Nowadays eye states are used as inputs to various applications such as facial expression recognition systems, human-computer interaction and driver fatigue detection systems. Especially with the pervasion of human computer interaction, eye state detection has drawn great attention in the past decade. In this study, an eye state detection system based on circular Hough transform has been offered. Initially, a face image is extracted from a given image. Eye pair images are obtained from this face image, and eyes are acquired from the eye pair images. After preprocessing, existence of circular iris structure is searched with the help of circular Hough transfrom within the eye image. Eyes are decided as open if iris is visible.
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    Eye Location and Eye State Detection in Facial Images Using Circular Hough Transform
    (Springer-Verlag Berlin, 2013) Soylemez, Omer Faruk; Ergen, Burhan
    Recently, eye states are used as inputs to various applications such as facial expression recognition systems, human-computer interaction and driver fatigue detection systems. Especially with the prominence of human computer interaction, eye state detection has drawn great attention in the past decade. In this study, an eye state detection system based on Circular Hough Transform (CHT) has been offered. Initially, face and eye images are extracted from given gray-level images. After some preprocessing steps, existence of circular iris structure is searched within the extracted eye image using CHT. Existence of circular iris structure is searched within the eye image with the help of circular Hough transform. Eyes are decided as open if iris could identified as a circle.
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    Öğe
    Facial Emotion Recognition on a Dataset Using Convolutional Neural Network
    (Ieee, 2017) Tumen, Vedat; Soylemez, Omer Faruk; Ergen, Burhan
    Nowadays, deep learning is a technique that takes place in many computer vision related applications and studies. While it is put in the practice mostly on content based image retrieval, there is still room for improvement by employing it in diverse computer vision applications. In this study, we aimed to build a Convolutional Neural Network (CNN) based Facial Expression Recognition System (FER), in order to automatically classify expressions presented in Facial Expression Recognition (FER2013) database. Our presented CNN achieved % 57.1 success rate on FER2013 database.