Yazar "Ozdemir, Mehmet Fatih" seçeneğine göre listele

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    Deep Feature Selection for Facial Emotion Recognition Based on BPSO and SVM
    (Gazi Univ, 2023) Donuk, Kenan; Ari, Ali; Ozdemir, Mehmet Fatih; Hanbay, Davut
    Facial expressions, which are important social communication tools in our daily life, provide important information about the mental state of people. Research is being done to obtain this information accurately. The importance of these researchs in the field of human-computer interaction is increasing. Many methods have been used for the recognition of universal facial expressions such as neutral, happiness, surprise, sadness, anger, disgust, and fear by intelligent systems with high accuracy. Emotion recognition is an example of difficult classification due to factors such as ambient light, age, race, gender, and facial position. In this article, a 3-stage system is proposed for emotion detection from facial images. In the first stage, the CNN-based network is trained with the Fer+ dataset. The Binary Particle Swarm Optimization algorithm is applied for feature selection to the feature vector in the fully connected layer of the CNN network trained in the second stage. Selected features are classified by Support Vector Machine. The performance of the proposed system has been tested with the Fer+ dataset. As a result of the test, 85.74% accuracy was measured. The results show that the combination of BPSO and SVM contributes to the classification accuracy and speed of the FER+ dataset.
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    Detection of Traffic Light Violations from Fish Eye Driver Cameras of Public Transport Buses
    (Ieee, 2023) Ercan, Burak; Ozdemir, Mehmet Fatih; Eker, Onur; Baytekin, Mehmet Can; Bal, Murat
    Nowadays, automatic detection of traffic rule violations become an important part of smart transportation networks. Among these, automatic detection of traffic light violations is one of the most needed due to their serious effects on life and property safety. In this study, we propose a method based on computer vision and deep learning techniques for the automatic detection of traffic light violations of public transport buses. Our method is designed to work with images taken from fish eye driver cameras, which are currently used for security purposes in buses, in order not to create the need for additional equipment. With our experiments, it has been shown that the object detection models in the literature and in addition, a simple method using optical flow are insufficient for this problem, whereas our proposed method produces successful results.