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Full-Text Articles in Engineering
An Evolutionary-Based Image Classification Approach Through Facial Attributes, Seli̇m Yilmaz, Cemi̇l Zalluhoğlu
An Evolutionary-Based Image Classification Approach Through Facial Attributes, Seli̇m Yilmaz, Cemi̇l Zalluhoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
With the recent developments in technology, there has been a significant increase in the studies on analysisof human faces. Through automatic analysis of faces, it is possible to know the gender, emotional state, and even theidentity of people from an image. Of them, identity or face recognition has became the most important task whichhas been studied for a long time now as it is crucial to take measurements for public security, credit card verification,criminal identification, and the like. In this study, we have proposed an evolutionary-based framework that relies ongenetic programming algorithm to evolve a binary- and multilabel image classifier …
The Nearest Polyhedral Convex Conic Regions For High-Dimensional Classification, Hakan Çevi̇kalp, Emre Çi̇men, Gürkan Öztürk
The Nearest Polyhedral Convex Conic Regions For High-Dimensional Classification, Hakan Çevi̇kalp, Emre Çi̇men, Gürkan Öztürk
Turkish Journal of Electrical Engineering and Computer Sciences
In the nearest-convex-model type classifiers, each class in the training set is approximated with a convexclass model, and a test sample is assigned to a class based on the shortest distance from the test sample to these classmodels. In this paper, we propose new methods for approximating the distances from test samples to the convex regionsspanned by training samples of classes. To this end, we approximate each class region with a polyhedral convex conicregion by utilizing polyhedral conic functions (PCFs) and its extension, extended PCFs. Then, we derive the necessary formulations for computing the distances from test samples to these …
Presentation Attack Detection For Face Recognition Using Remotephotoplethysmography And Cascaded Fusion, Mehmet Fati̇h Gündoğar, Çi̇ğdem Eroğlu Erdem
Presentation Attack Detection For Face Recognition Using Remotephotoplethysmography And Cascaded Fusion, Mehmet Fati̇h Gündoğar, Çi̇ğdem Eroğlu Erdem
Turkish Journal of Electrical Engineering and Computer Sciences
Spoofing (presentation) attacks are important threats for face recognition and authentication systems, which try to deceive them by presenting an image or video of a different subject, or by using a 3D mask. Remote (non-contact) photoplethysmography (rPPG) is useful for liveness detection using a facial video by estimating the heart-rate of the subject. In this paper, we first compare the presentation attack detection performance of three different rPPG-based heart rate estimation methods on four datasets (3DMAD, Replay-Attack, Replay-Mobile, and MSU-MFSD). We also present a cascaded fusion system, which utilizes a multistage ensemble of classifiers using rPPG, motion-based (including head-pose, eye-gaze …
Sample Group And Misplaced Atom Dictionary Learning For Face Recognition, Meng Wang, Zhengping Hu, Zhe Sun, Mei Zhu, Mei Sun
Sample Group And Misplaced Atom Dictionary Learning For Face Recognition, Meng Wang, Zhengping Hu, Zhe Sun, Mei Zhu, Mei Sun
Turkish Journal of Electrical Engineering and Computer Sciences
Latest research results have demonstrated the effectiveness of both sparse (or collaborative) representation and dictionary learning for problem solving in face recognition and other signal classification. Considering the fact that an informative dictionary helps a lot in sparse coding, a novel model that consists of group dictionary learning and high-quality joint kernel collaborative representation was proposed in this paper, where rich information from original and virtual space was mined and constructed as a sample group space to improve classification accuracy. Meanwhile, joint kernel collaborative representation with an $\ell_{2}$-regularization-based classifier was used to capture more nonlinear structure and minimize the time …
A Comprehensive Comparison Of Features And Embedding Methods For Face Recognition, Hasan Serhan Yavuz, Hakan Çevi̇kalp, Ri̇fat Edi̇zkan
A Comprehensive Comparison Of Features And Embedding Methods For Face Recognition, Hasan Serhan Yavuz, Hakan Çevi̇kalp, Ri̇fat Edi̇zkan
Turkish Journal of Electrical Engineering and Computer Sciences
Face recognition is an essential issue in modern-day applications since it can be used in many areas for several purposes. Many methods have been proposed for face recognition. It is a difficult task since variations in lighting, instantaneous mimic varieties, posing angles, and scaling differences can drastically change the appearance of the face. To suppress these complications, effective feature extraction and proper alignment of face images gain as much importance as the recognition method choice. In this paper, we provide an extensive comparison of the state-of-the-art face recognition methods with the most well-known techniques used in feature representation. In order …
Co-Occurrence Matrix And Its Statistical Features As A New Approach For Face Recognition, Alaa Eleyan, Hasan Demirel
Co-Occurrence Matrix And Its Statistical Features As A New Approach For Face Recognition, Alaa Eleyan, Hasan Demirel
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a new face recognition technique is introduced based on the gray-level co-occurrence matrix (GLCM). GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. We proposed two methods to extract feature vectors using GLCM for face classification. The first method extracts the well-known Haralick features from the GLCM, and the second method directly uses GLCM by converting the matrix into a vector that can be used in the classification process. The results demonstrate that the second method, which uses GLCM directly, is superior to the first method that …
An Algorithm To Minimize Within-Class Scatter And To Reduce Common Matrix Dimension For Image Recognition, Ümi̇t Çi̇ğdem Turhal, Alpaslan Duysak
An Algorithm To Minimize Within-Class Scatter And To Reduce Common Matrix Dimension For Image Recognition, Ümi̇t Çi̇ğdem Turhal, Alpaslan Duysak
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a new algorithm using 2DPCA and Gram-Schmidt Orthogonalization Procedure for recognition of face images is proposed. The algorithm consists of two parts. In the first part, a common feature matrix is obtained; and in the second part, the dimension of the common feature matrix is reduced. Resulting common feature matrix with reduced dimension is used for face recognition. Column and row covariance matrices are obtained by applying 2DPCA on the column and row vectors of images, respectively. The algorithm then applies eigenvalue-eigenvector decomposition to each of these two covariance matrices. Total scatter maximization is achieved taking the …