Yazar "Söylemez, Ömer Faruk" seçeneğine göre listele

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    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.
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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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    Forward selection-based ensemble of deep neural networks for melanoma classification in dermoscopy images
    (Wiley, 2023) Söylemez, Ömer Faruk; 0000-0002-4076-5230
    Melanoma is a rare skin cancer that constitutes only 1% of skin cancer cases. However, its ability to spread to other organs makes it deadliest among the four major cancer types. Early diagnosis of melanoma is essential, as it prevents cancer from spreading to other body parts, therefore significantly reducing mortality rates. In this study, we presented a forward selection-based ensembling strategy for deep neural networks to aid the diagnosis of melanoma in dermoscopy images. The proposed approach uses an ensemble of neural networks with varying input sizes to effectively capture size-related various properties of dermoscopy images. To this end, EfficientNet models B3-B7 are used with input resolutions of 256, 384, 512, and 768. Training and validation are carried out in a triple stratified cross-validation style with folds providing patient isolation, balance in the percentage of classes and balanced patient count distribution. Ensembles are formed by a modified form of forward selection algorithm. Experimental results show that the AUC for classification is increased by 2.01% using the proposed ensembling scheme.
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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.