Browsing by Author "Ekim P.O."

Now showing 1 - 3 of 3

Citation - Scopus: 1
Face Detection Via Hog and Ga Feature Selection With Support Vector Machines
(Institute of Electrical and Electronics Engineers Inc., 2019) Binli M.K.; Can Demiryilmaz B.; Ekim P.O.; Yeganli F.
This work employs a new method of feature selection and classification of image objects by combining previous studies in literature of feature selection and classification of images. In the new algorithm, instead of sliding a window on the image and scaling the window by applying normalized cuts and image segmentation algorithms, information related to the position of objects is considered. Accordingly, the scaling of searching image window process is been exceeded. For this purpose, the obtained segments of image features extracted by employing Histogram Oriented Gradient (HOG) descriptor, which clears the images from the curse of dimensions. These extracted features optimized by applying Genetic Algorithm (GA) method, to detect faces and compared with HOG feature results. The main objective of this work is to improve the accuracy of Support Vector Machine (SVM) classifier in detail description applications. © 2019 Chamber of Turkish Electrical Engineers.
Citation - Scopus: 4
Face Detection, Tracking and Recognition With Artificial Intelligence
(Institute of Electrical and Electronics Engineers Inc., 2021) Tekkok S.C.; Soyunmez M.E.; Bostanci B.; Ekim P.O.
Due to the COVID-19 pandemic, the face masks are mandatory everywhere so it might be beneficial to automatize the detection and tracking of whether people wear a mask or not with the help of the computer vision. Furthermore, it can be implemented on mobile robots as well. Additionally, face recognition of people is discussed with two different techniques which are based on neural networks and eigenface approach in this study. Hence, a complete system which can be used with mobile platforms has been achieved with the proposed procedure. © 2021 IEEE.
Citation - Scopus: 3
Real-Time Facial Emotion Recognition for Visualization Systems
(Institute of Electrical and Electronics Engineers Inc., 2022) Ozkara C.; Ekim P.O.
This project aims to review the most popular deep learning algorithms and their performances in camera systems based on real-time facial emotion recognition and suggest a new model for future applications. Firstly, convolutional neural network (CNN) algorithms that recognize human emotions, such as AlexNet, GoogleNet, and VGG19, are investigated according to their performances. Then, the CNN algorithm with the best numerical performance is chosen for enhancement. After, the new hybrid model is constructed via chosen CNN and long short-term memory (LSTM). Lastly, the proposed model and face images achieved from the camera are combined to simulate real-time application. © 2022 IEEE.