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Yayın Analysis of single image super resolution models(IEEE, 2022-11-18) Köprülü, Mertali; Eskil, Mustafa TanerImage Super-Resolution (SR) is a set of image processing techniques which improve the resolution of images and videos. Deep learning approaches have made remarkable improvement in image super-resolution in recent years. This article aims and seeks to provide a comprehensive analysis on recent advances of models which has been used in image superresolution. This study has been investigated over other essential topics of current model problems, such as publicly accessible benchmark data-sets and performance evaluation measures. Finally, The study concluded these analysis by highlighting several weaknesses of existing base models as their feeding strategy and approved that the training technique which is Blind Feeding, which led several model to achieve state-of-the art.Yayın Closeness and uncertainty aware adversarial examples detection in adversarial machine learning(Elsevier Ltd, 2022-07) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerWhile deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These vulnerabilities make it challenging to deploy Deep Neural Network (DNN) models in security-critical areas. Recently, many research studies have been conducted to develop defense techniques enabling more robust models. In this paper, we target detecting adversarial samples by differentiating them from their clean equivalents. We investigate various metrics for detecting adversarial samples. We first leverage moment-based predictive uncertainty estimates of DNN classifiers derived through Monte-Carlo (MC) Dropout Sampling. We also introduce a new method that operates in the subspace of deep features obtained by the model. We verified the effectiveness of our approach on different datasets. Our experiments show that these approaches complement each other, and combined usage of all metrics yields 99 % ROC-AUC adversarial detection score for well-known attack algorithms.Yayın Convolutional neural network (CNN) algorithm based facial emotion recognition (FER) system for FER-2013 dataset(IEEE, 2022-11-18) Ezerceli, Özay; Eskil, Mustafa TanerFacial expression recognition (FER) is the key to understanding human emotions and feelings. It is an active area of research since human thoughts can be collected, processed, and used in customer satisfaction, politics, and medical domains. Automated FER systems had been developed and have been used to recognize humans’ emotions but it has been a quite challenging problem in machine learning due to the high intra-class variation. The first models were using known methods such as Support Vector Machines (SVM), Bayes classifier, Fuzzy Techniques, Feature Selection, Artificial Neural Networks (ANN) in their models but still, some limitations affect the accuracy critically such as subjectivity, occlusion, pose, low resolution, scale, illumination variation, etc. The ability of CNN boosts FER accuracy. Deep learning algorithms have emerged as the greatest way to produce the best results in FER in recent years. Various datasets were used to train, test, and validate the models. FER2013, CK+, JAFFE and FERG are some of the most popular datasets. To improve the accuracy of FER models, one dataset or a mix of datasets has been employed. Every dataset includes limitations and issues that have an impact on the model that is trained for it. As a solution to this problem, our state-of-the-art model based on deep learning architectures, particularly convolutional neural network architectures (CNN) with supportive techniques has been implemented. The proposed model achieved 93.7% accuracy with the combination of FER2013 and CK+ datasets for FER2013.Yayın Driver recognition using gaussian mixture models and decision fusion techniques(Springer-Verlag Berlin, 2008) Benli, Kristin Surpuhi; Düzağaç, Remzi; Eskil, Mustafa TanerIn this paper we present our research in driver recognition. The goal of this study is to investigate the performance of different classifier fusion techniques in a driver recognition scenario. We are using solely driving behavior signals such as break and accelerator pedal pressure, engine RPM, vehicle speed; steering wheel angle for identifying the driver identities. We modeled each driver using Gaussian Mixture Models, obtained posterior probabilities of identities and combined these scores using different fixed mid trainable (adaptive) fusion methods. We observed error rates is low as 0.35% in recognition of 100 drivers using trainable combiners. We conclude that the fusion of multi-modal classifier results is very successful in biometric recognition of a person in a car setting.Yayın El yazısı rakam sınıflandırması için gözetimsiz benzerlik tabanlı evrişimler(Institute of Electrical and Electronics Engineers Inc., 2022) Erkoç, Tuğba; Eskil, Mustafa TanerEffective training of filters in Convolutional Neural Networks (CNN) ensures their success. In order to achieve good classification results in CNNs, filters must be carefully initialized, trained and fine-tuned. We propose an unsupervised method that allows the discovery of filters from the given dataset in a single epoch without specifying the number of filters hyper-parameter in convolutional layers. Our proposed method gradually builds the convolutional layers by a discovery routine that extracts a number of features that adequately represent the complexity of the input domain. The discovered filters represent the patterns in the domain, so they do not require any initialization method or backpropagation training for fine tuning purposes. Our method achieves 99.03% accuracy on MNIST dataset without applying any data augmentation techniques.Yayın Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples(Springer, 2022-03) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerDeep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, termed as adversarial samples. In recent years, numerous studies have been conducted in this new area called ``Adversarial Machine Learning” to devise new adversarial attacks and to defend against these attacks with more robust DNN architectures. However, most of the current research has concentrated on utilising model loss function to craft adversarial examples or to create robust models. This study explores the usage of quantified epistemic uncertainty obtained from Monte-Carlo Dropout Sampling for adversarial attack purposes by which we perturb the input to the shifted-domain regions where the model has not been trained on. We proposed new attack ideas by exploiting the difficulty of the target model to discriminate between samples drawn from original and shifted versions of the training data distribution by utilizing epistemic uncertainty of the model. Our results show that our proposed hybrid attack approach increases the attack success rates from 82.59% to 85.14%, 82.96% to 90.13% and 89.44% to 91.06% on MNIST Digit, MNIST Fashion and CIFAR-10 datasets, respectively.Yayın Extraction and selection of muscle based features for facial expression recognition(IEEE Computer Soc, 2014-12-04) Benli, Kristin Surpuhi; Eskil, Mustafa TanerIn this study we propose a new set of muscle activity based features for facial expression recognition. We extract muscular activities by observing the displacements of facial feature points in an expression video. The facial feature points are initialized on muscular regions of influence in the first frame of the video. These points are tracked through optical flow in sequential frames. Displacements of feature points on the image plane are used to estimate the 3D orientation of a head model and relative displacements of its vertices. We model the human skin as a linear system of equations. The estimated deformation of the wireframe model produces an over-determined system of equations that can be solved under the constraint of the facial anatomy to obtain muscle activation levels. We apply sequential forward feature selection to choose the most descriptive set of muscles for recognition of basic facial expressions.Yayın Facial expression recognition based on anatomy(Academic Press Inc Elsevier Science, 2014-02) Eskil, Mustafa Taner; Benli, Kristin SurpuhiIn this study, we propose a novel approach to facial expression recognition that capitalizes on the anatomical structure of the human face. We model human face with a high-polygon wireframe model that embeds all major muscles. Influence regions of facial muscles are estimated through a semi-automatic customization process. These regions are projected to the image plane to determine feature points. Relative displacement of each feature point between two image frames is treated as an evidence of muscular activity. Feature point displacements are projected back to the 3D space to estimate the new coordinates of the wireframe vertices. Muscular activities that would produce the estimated deformation are solved through a least squares algorithm. We demonstrate the representative power of muscle force based features on three classifiers; NB, SVM and Adaboost Ability to extract muscle forces that compose a facial expression will enable detection of subtle expressions, replicating an expression on animated characters and exploration of psychologically unknown mechanisms of facial expressions.Yayın Factored particle filtering with dependent and constrained partition dynamics for tracking deformable objects(Springer, 2014-10) Eskil, Mustafa TanerIn particle filtering, dimensionality of the state space can be reduced by tracking control (or feature) points as independent objects, which are traditionally named as partitions. Two critical decisions have to be made in implementation of reduced state-space dimensionality. First is how to construct a dynamic (transition) model for partitions that are inherently dependent. Second critical decision is how to filter partition states such that a viable and likely object state is achieved. In this study, we present a correlation-based transition model and a proposal function that incorporate partition dependency in particle filtering in a computationally tractable manner. We test our algorithm on challenging examples of occlusion, clutter and drastic changes in relative speeds of partitions. Our successful results with as low as 10 particles per partition indicate that the proposed algorithm is both robust and efficient.Yayın İfade tanıma için yüz anatomisine dayalı öznitelikler(IEEE, 2014-04-23) Benli, Kristin Surpuhi; Eskil, Mustafa TanerBu çalışmada yüz ifadesi tanıma için kas kuvvetlerine dayalı yeni öznitelikler öneriyoruz. Yüz üzerinde seçtiğimiz noktaların video üzerindeki hareketlerini izleyerek kas kuvvetlerini çözüyoruz. Yüz noktaları, ilk video çerçevesi üzerinde, kas kuvvet alanları üzerinde ilklendirilir. Bu noktalar optik akış algoritması ile izlenir. Noktaların devinimleri yüzün 3 boyutlu yönelimi ve yüz ifadesine dayalı bağıl devinimleri kestirmek için kullanılır. İnsan yüzünü yaylarla, artık-belirtilmiş doğrusal bir denklem sistemi olarak modelliyoruz. Bu sistemi yüz anatomisi kısıtı altında, kas kuvvetleri için çözüyoruz. Ardışık ileri seçim yaparak, temel yüz ifadeleri için en betimleyici kas kümesini belirliyoruz.Yayın Improved microphone array design with statistical speaker verification(Elsevier Ltd, 2021-04) Demir, Kadir Erdem; Eskil, Mustafa TanerConventional microphone array implementations aim to lock onto a source with given location and if required, tracking it. It is a challenge to identify the intended source when the location of the source is unknown and interference exists in the same environment. In this study we combine speaker verification and microphone array processing techniques to localize and maximize gain on the intended speaker under the assumption of open acoustic field. We exploit the steering capability of the microphone array for more accurate speaker verification. Our first contribution is a new N-Gram based and computationally efficient feature for detecting an intended speaker. When the source and interference are localized, microphone array can be tuned further to reduce noise and increase the gain. Our second contribution is this integrated algorithm for speaker verification and localization. In the context of this study we developed SharpEar, an open source environment that simulates propagation of sound emanating from multiple sources. Our third and last contribution is this simulation environment, which is open source and available to researchers of the field.Yayın Integrating vendors into cooperative design practices(Taylor & Francis Ltd, 2009) Eskil, Mustafa Taner; Sticklen, JonThis paper describes a new approach to cooperative design using distributed, off-the-shelf design components. The ultimate goal is to enable assemblers to rapidly design their products and perform simulations using parts that are offered by a global network of suppliers. The obvious way to realise this goal would be to transfer desired component models to the client computer. However, in order to protect proprietary data, manufacturers are reluctant to share their design models without non-disclosure agreements, which can take in the order of months to put in place. Due to bandwidth limitations, it is also impractical to keep the models at the manufacturer site and do simulations by simple message passing. To deal with these impediments in e-commerce the modular distributed modelling (MDM) methodology is leveraged, which enables transfer of component models while hiding proprietary implementation details. MDM methodology with routine design (RD) methods are augmented to realise a platform (RD-MDM) that enables automatic selection of secured off-the-shelf design components over the Internet, integration of these components in an assembly, running simulations for design testing and publishing the approved product model as a secured MDM agent. This paper demonstrates the capabilities of the RD-MDM platform on a fuel cell-battery hybrid vehicle design example.Yayın Nearest neighbor weighted average customization for modeling faces(Springer, 2013-10) Abeysundera, Hasith Pasindu; Benli, Kristin Surpuhi; Eskil, Mustafa TanerIn this paper, we present an anatomically accurate generic wireframe face model and an efficient customization method for modeling human faces. We use a single 2D image for customization of the generic model. We employ perspective projection to estimate 3D coordinates of the 2D facial landmarks in the image. The non-landmark vertices of the 3D model are shifted using the translations of k nearest landmark vertices, inversely weighted by the square of their distances. We demonstrate on Photoface and Bosphorus 3D face data sets that the proposed method achieves substantially low relative error values with modest time complexity.Yayın Numerical integration methods for simulation of mass-spring-damper systems(Springer-Verlag, 2012) Özgüz, Mete; Eskil, Mustafa TanerThe dynamics of a face are often implemented as a system of connected particles with various forces acting upon them. Animation of such a system requires the approximation of velocity and position of each particle through numerical integration. There are many numerical integrators that are commonly used in the literature. We conducted experiments to determine the suitability of numerical integration methods in approximating the particular dynamics of mass-spring-damper systems. Among Euler, semi-implicit Euler, Runge-Kutta and Leapfrog, we found that simulation with Leapfrog numerical integration characterizes a mass-spring-damper system best in terms of the energy loss of the overall system.Yayın An observation based muscle model for simulation of facial expressions(Elsevier Science BV, 2018-05) Erkoç, Tuğba; Ağdoğan, Didem; Eskil, Mustafa TanerThis study presents a novel facial muscle model for coding of facial expressions. We derive this model from unintrusive observation of human subjects in the progress of the surprise expression. We use a generic and single-layered face model which embeds major muscles of the human face. This model is customized onto the human subject's face on the first frame of the video. The last frame of the video is used to project a set of manually marked feature points to estimate the 3 dimensional displacements of vertices due to facial expression. Vertex displacements are used in a mass spring model to estimate the external forces, i.e. the muscle forces on the skin. We observed that the distribution of muscle forces resemble sigmoid or hyperbolic tangent functions. We chose hyperbolic tangent function as our base model and parameterized it using least squares. We compared the proposed muscle model with frequently used models in the literature.Yayın Palmprint verification using SIFT majority voting(Springer-Verlag, 2012) Abeysundera, Hasith Pasindu; Eskil, Mustafa TanerIn this paper we illustrate the implementation of a robust, real-time biometric system for identity verification based on palmprint images. The palmprint images are preprocessed to align the major axes of hand shapes and to extract the palm region. We extract features using Scale Invariant Feature Transform (SIFT). Classification of individual SIFT features is done through KNN. The class of the hand image is decided by a majority based voting among its classified SIFT features. We demonstrate on the CASIA and PolyU datasets that the proposed system achieves authentication accuracy comparable to other state of the art algorithms.Yayın Raylı sistemlerde yüksek gerilim aksamının otomatik denetimi(IEEE, 2014-04-23) Ağdoğan, Didem; Babacan, Veysel Karani; Eskil, Mustafa TanerRaylı sistemlerde yolculugun sorunsuz tamamla-nabilmesi için sistem bütünlüğü kritik öneme sahiptir. Sistem bütünlüğü, lokomotif ve vagonlar haricinde katener (yüksek gerilim) hattı, pantograf ve raylara bağlıdır. Katener hattı ve pantograf, lokomotife elektrik iletimini sağlarken rayların seviyesi pantografın elektrik hattına düzenli temasına etki eder. Raylarda oluşabilecek çöküntüler katener hattı ile pantograf arasında ark (kıvılcım) oluşumuna neden olur. Katener hattının pantograf sınırları dışına çıkması, pantografta oluşabilecek çentikler ve ark oluşumu lokomotif üzerinden anlık izlenebilir. Bu çalışmada amacımız, bu üç ögeden kaynaklanabilecek hataları kameralı bir sistemle, gerçek zamanlı ve otomatik izleyerek tren yolculuğunun güvenli ve kesintisiz yapılmasına katkıda bulunmaktır.Yayın The routine design-modular distributed modeling platform for distributed routine design and simulation-based testing of distributed assemblies(Cambridge University Press, 2008-12-12) Eskil, Mustafa Taner; Sticklen, Jon; Radcliffe, ClarkIn this paper we describe a conceptual framework and implementation of a tool that supports task-directed, distributed routine design (RD) augmented with simulation-based design testing. In our research, we leverage the modular distributed modeling (MDM) methodology to simulate the interaction of design components in an assembly. The major improvement we have made in the RD methodology is to extend it with the capabilities of incorporating remotely represented off-the-shelf components in design and simulation-based testing of a distributed assembly. The deliverable of our research is the RD-MDM platform, which is capable of automatically selecting intellectually protected off the shelf design components over the Internet, integrating these components in an assembly, running simulations for design testing, and publishing the approved design without disclosing the proprietary information.Yayın Semi-automatic adaptation of high-polygon wireframe face models through inverse perspective projection(Springer-Verlag, 2012) Benli, Kristin Surpuhi; Ağdoğan, Didem; Özgüz, Mete; Eskil, Mustafa TanerPrecise registration of a generic 3D face model with a subject's face is a critical stage for model based analysis of facial expressions. In this study we propose a semi-automatic model fitting algorithm to fit a high-polygon wireframe model to a single image of a face. We manually mark important landmark points both on the wireframe model and the face image. We carry out an initial alignment by translating and scaling the wireframe model. We then translate the landmark vertices in the 3D wireframe model so that they coincide with inverse perspective projections of image landmark points. The vertices that are not manually labeled as landmark are translated with a weighted sum of vectorial displacement of k neighboring landmark vertices, inversely weighted by their 3D distances to the vertex under consideration. Our experiments indicate that we can fit a high-polygon model to the subject's face with modest computational complexity.
