Yazar "Ergen, Burhan" seçeneğine göre listele

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    A 3D Facial Expression Recognition System Based On SVM Classifier Using Distance Based Features
    (Ieee, 2017) Soylemez, Omer Famk; Ergen, Burhan; Soylemez, Nesrin Hark
    In this study, an SVM-based system is proposed for the classification of facial expressions that are represented in 3D. Distance based features are used as a feature vector, which are determined by the distances between the different key points on the image. Study was conducted on a subset (Happy, sadness, surprise) of Bosphorus 3D Face Database. 9 different fiducial points arc used to calculate a total of 5 distance features. SVM classification was performed with K-fold cross validation thus mean classification performance of different training and test clusters were determined. %85 success rate has achieved as a result of the expression analysis performed on the 3D facial scans.
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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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    Öğe
    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.
  • Küçük Resim
    Öğe
    Eye location and eye state detection in facial images using circular Hough transform
    (2013) Söylemez, Ömer Faruk; Ergen, Burhan; 0000-0002-4076-5230
    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.
  • [ X ]
    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.
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    Facial landmark based region of interest localization for deep facial expression recognition
    (Univ Osijek, 2022) Söylemez, Ömer Faruk; Ergen, Burhan
    Automated facial expression recognition has gained much attention in the last years due to growing application areas such as computer animated agents, sociable robots and human computer interaction. The realization of a reliable facial expression recognition system through machine learning is still a challenging task particularly on databases with large number of images. Convolutional Neural Network (CNN) architectures have been proposed to deal with large numbers of training data for better accuracy. For CNNs, a task related best achieving architectural structure does not exist. In addition, the representation of the input image is equivalently important as the architectural structure and the training data. Therefore, this study focuses on the performances of various CNN architectures trained by different region of interests of the same input data. Experiments are performed on three distinct CNN architectures with three different crops of the same dataset. Results show that by appropriately localizing the facial region and selecting the correct CNN architecture it is possible to boost the recognition rate from 84% to 98% while decreasing the training time for proposed CNN architectures.
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    İnsan yüzü imgelerinde dairesel hough dönüşümü tabanlı göz durumu tespiti
    (2013) Söylemez, Ömer 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.