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Yayın Driver recognition and driver verification using data mining technigues(Işık Üniversitesi, 2007-09-25) Benli, Kristin Surpuhi; Eskil, Mustafa Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans ProgramıIn this thesis we present our research in driver recognition and driver verification. The goal of this study is to investigate the affect of different classifier fusion techniques on the performance of driver recognition and driver verification. We are using five different driving behavior signals for identifying the driver identities. Driving features were extracted from these signals and Gaussian Mixture Models were used for modeling the driver behavior. Gaussian Mixture Model training was performed using the well-known EM algorithm. In recognition study posterior probabilities of identities called scores were obtained with the given test data. These scores were combined using different fixed and trainable (adaptive) combination methods. In verification study we compared posterior probabilities with fixed threshold values for each classifier. For different thresholds, false-accept rate versus falsereject rate was plotted using the receiver operating characteristics curve. We observed lower error rates when we used trainable combiners. We conclude that combined multi-modal signal or classifier methods are very successful in biometric recognition and verification of a person in a car environment.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 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 Facial expression recognition based on facial anatomy(Işık Üniversitesi, 2013-06-06) Benli, Kristin Surpuhi; Eskil, Mustafa Taner; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Doktora ProgramıIn this thesis we propose to determine the underlying muscle forces that compose a facial expression under the constraint of facial anatomy. Muscular activities are novel features that are highly representative of facial expressions. We model human face with a 3D generic wireframe model that embeds all major muscles. The input to our expression recognition system is a video with marked set of landmark points on the first frame. We use these points and a semi-automatic fitting algorithm to register the 3D face model to the subject's face. The influence regions of facial muscles are estimated and projected to the image plane to determine feature points. These points are tracked on the image plane using optical flow algorithm. We estimate the rigid body transformation of the head through a greedy search algorithm. This stage enables us to align the 3D face model with the subject's head in consecutive frames of the video. We use ray tracing from the perspective reference point and through the image plane to estimate the new coordinates of model vertices. The estimated vertex coordinates indicate how the subject's face is deformed in the progression of an expression. The relative motion of model vertices provides us an over-determined linear system of equations where unknown parameters are the muscle activation levels. This system of equations is solved using constrained least square optimization. Muscle activity based features are evaluated in a classification problem of seven basic facial expressions. We demonstrate the representative power of muscle force based features on four classifiers; Linear Discriminant Analysis, Naive Bayes, k-Nearest Neighbor and Support Vector Machine. The best performance on the classification problem of seven expressions including neutral was 87.1 %, obtained by use of Support Vector Machine. The results we attained in this study are close to the human recognition ceiling of 87-91.7 % and comparable with the state of the art algorithms in the literature.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 Implicit theories and self-efficacy in an introductory programming course(Institute of Electrical and Electronics Engineers Inc, 2018-08) Tek, Faik Boray; Benli, Kristin Surpuhi; Deveci, EzgiContribution: This paper examined student effort and performance in an introductory programming course with respect to student-held implicit theories and self-efficacy. Background: Implicit theories and self-efficacy help in understanding academic success, which must be considered when developing effective learning strategies for programming.Research Questions: Are implicit theories of intelligence and programming, and programming-efficacy, related to each other and to student success in programming? Is it possible to predict student performance in a course using these constructs? Methodology: Two consecutive surveys ({N}=100 and {N}=81) were administered to non-CS engineering students in Işik University, Turkey. Findings: Implicit theories of programming-aptitude and programming-efficacy are interrelated and positively correlated with effort, performance, and previous failures in the course. Although it was not possible to predict student course grade the data confirms that students who believe in improvable programming aptitude have significantly higher programming efficacy, report more effort, and get higher course grades. In addition, failed students tend to associate the failure with fixed programming aptitude; repeating students favor fixed programming aptitude theory and have lower programming-efficacy, which increases the possibility of further failure.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 Programlamaya giriş dersi öğrencilerinin öz yeterlilik algıları ve derse yönelik tutumlarının cinsiyet ve eğitim diline göre incelenmesi(IEEE, 2017-10-31) Deveci, Ezgi; Aydın, Damla; Benli, Kristin Surpuhi; Tek, Faik BorayBu araştırmanın amacı F.M.V. Işık Üniversitesi Mühendislik Fakültesinde öğrenim gören öğrencilerin genel öz-yeterlilik algılarının ve Programlamaya Giriş(CSE101) dersine yönelik tutumlarının; cinsiyet ve eğitim aldıkları programın diline (Türkçe-İngilizce) göre incelenmesidir. Araştırmaya 40 kadın ve 74 erkek olmak üzere toplam 114 üniversite öğrencisi katılmıştır. Öğrencilerin öz yeterlilik algılarını ölçmek için Genel Öz Yeterlilik ölçeği kullanılmış, ders sonucunu (başarı ve başarısızlık) değerlendirmeleri için açık uçlu sorular sorulmuş ve yaş, cinsiyet gibi temel demografik bilgileri alınmıştır. Açık uçlu sorular niteliksel (kalitatif) analiz yöntemi ile incelenmiştir. Yapılan niceliksel analiz sonucunda öğrencilerin genel öz-yeterlilik puanları ile genel not ortalaması arasında anlamlı, CSE101 dersi dönem sonu not ortalaması arasında ise anlamsız bir ili ki olduğu bulgulanmıştır. Ayrıca öğrencilerin öz-yeterlilik puanlarının cinsiyete ve eğitim aldıkları dile göre (Türkçe-İngilizce) değişmediği görülmüştür. Öğrencilerin motivasyon puanları da eğitim aldıkları dile göre farklılaşmamaktadır. Niteliksel analiz bulgularına göre ise öğrencilerin verdiği cevapların yüzde sıklık değerlerinin cinsiyetleri açısından değiştiği görülmüştür. Bu çalışmanın sonuçları özellikle öğrencilerin derse yönelik tutumlarında cinsiyet açısından bir farklılık olduğunu göstermesi ile mühendislik programlama eğitiminde öğrenci başarısını yordayan değişkenlerin tespit edilmesi sürecine katkı sağlaması beklenmektedir.Yayın Programlamaya giriş dersini alan öğrencilerin programlama öz yeterlilik algılarının ve programlamaya bakış açılarının incelenmesi(Düzce Üniversitesi, 2021-05-29) Benli, Kristin Surpuhi; Tek, Faik BorayBu çalışmada üniversite öğrencilerinin Java programlama öz yeterlilik algıları, programlama öğrenme istekleri ve çalışma alışkanlıkları çeşitli değişkenlere göre (cinsiyet, bölüm, eğitim dili, harf notu, ders tekrarları vb.) istatistiksel yöntemler kullanılarak (T-testi, Mann Whitney U-testi, Kruskal Wallis H testi, tek yönlü varyans analizi, Ki-Kare testi) incelenmiştir. Çalışma grubu, farklı bölümlerde zorunlu olarak programlamaya giriş dersini alan 191 lisans öğrencisinden oluşmaktadır. Elde edilen sonuçlara göre öğrencilerin Java programlama öz yeterlilik algıları bölümlerine ve programlama öğrenme isteklerine göre farklılaşmaktadır. Çalışmada ayrıca Apriori algoritması kullanılarak birliktelik kuralları çıkartılmıştır. En yüksek güven değeri elde edilen kurala göre, programlama öğrenmeyi çok fazla isteyen, programlama öğrenmenin iş hayatında kendisine fayda sağlayacağını düşünen ve programlama dersinden başarı ile geçen öğrencilerin programlama öz yeterlilikleri yüksektir.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.
