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Publication Metadata only Authoring and presentation tools for distance learning over interactive TV(2010) Gürel, Turan Can; Erdem, Tanju Tanju; Kermen, Ahmet; Özkan, Mehmet K.; Erdem, Cigdem Eroglu; Gürel, Turan Can, Momentum AS, Kocaeli, Turkey; Erdem, Tanju Tanju, Özyeğin Üniversitesi, Istanbul, Turkey; Kermen, Ahmet, Momentum AS, Kocaeli, Turkey; Özkan, Mehmet K., Momentum AS, Kocaeli, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyWe present a complete system for distance learning over interactive TV with novel tools for authoring and presentation of lectures and exams, and evaluation of student and system performance. The main technological contributions of the paper include the development of plug-in software so that PowerPoint can be used to prepare presentations for the set-top-box, a software tool to convert PDF documents containing multiple-choice questions into interactive exams, and a virtual teacher whose facial animation is automatically generated from speech. © 2013 Elsevier B.V., All rights reserved.Publication Metadata only INTERSPEECH 2009 emotion recognition challenge evaluation, INTERSPEECH 2009 duygu tanima yarişmasi deǧerlendirmesi(2010) Bozkurt, Elif; Erzin, Engin; Erdem, Cigdem Eroglu; Erdem, Tanju Tanju; Bozkurt, Elif, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Özyeğin Üniversitesi, Istanbul, TurkeyIn this paper we evaluate INTERSPEECH 2009 Emotion Recognition Challenge results. The challenge presents the problem of accurate classification of natural and emotionally rich FAU Aibo recordings into five and two emotion classes. We evaluate prosody related, spectral and HMM-based features with Gaussian mixture model (GMM) classifiers to attack this problem. Spectral features consist of mel-scale cepstral coefficients (MFCC), line spectral frequency (LSF) features and their derivatives, whereas prosody-related features consist of pitch, first derivative of pitch and intensity. We employ unsupervised training of HMM structures with prosody related temporal features to define HMM-based features. We also investigate data fusion of different features and decision fusion of different classifiers to improve emotion recognition results. Our two-stage decision fusion method achieves 41.59 % and 67.90 % recall rate for the five and two-class problems, respectively and takes second and fourth place among the overall challenge results. ©2010 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only RANSAC-based training data selection for emotion recognition from spontaneous speech(2010) Erdem, Cigdem Eroglu; Bozkurt, Elif; Erzin, Engin; Erdem, Tanju Tanju; Erdem, Cigdem Eroglu, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Bozkurt, Elif, Department of Electrical & Computer Engineering, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, Department of Electrical & Computer Engineering, Koç Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Department of Electrical & Computer Engineering, Özyeğin Üniversitesi, Istanbul, TurkeyTraining datasets containing spontaneous emotional expressions are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) based training approach for the problem of emotion recognition from spontaneous speech recordings. Our motivation is to insert a data cleaning process to the training phase of the Hidden Markov Models (HMMs) for the purpose of removing some suspicious instances of labels that may exist in the training dataset. Our experiments using HMMs with various number of states and Gaussian mixtures per state indicate that utilization of RANSAC in the training phase provides an improvement of up to 2.84% in the unweighted recall rates on the test set. This improvement in the accuracy of the classifier is shown to be statistically significant using McNemar's test. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only A comparison of geometrical facial features for affect recognition, Duygu tanima i̇çi̇n geometri̇k yüz özni̇teli̇kleri̇ni̇n karşilaştirilmasi(2011) Ulukaya, Sezer; Erdem, Cigdem Eroglu; Ulukaya, Sezer, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn this work, we compare two different geometric feature extraction methods derived from coordinates of facial points tracked by Active Appearance Models. The compared feature extraction methods differ in their use of coordinates or distances between facial points and whether they use the information of a neutral facial expression. Experiments on the extended Cohn-Kanade database show that the coordinate-based features using the neutral frame information gives the best emotion recognition results (%94) using a SVC classifier with a polynomial kernel. © 2011 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only Combining Haar feature and skin color based classifiers for face detection(2011) Erdem, Cigdem Eroglu; Ulukaya, Sezer; Karaali, Ali; Erdem, Tanju Tanju; Erdem, Cigdem Eroglu, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ulukaya, Sezer, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Karaali, Ali, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Electrical and Electronics Engineering, Özyeğin Üniversitesi, Istanbul, TurkeyThis paper presents a hybrid method for face detection in color images. The well known Haar feature-based face detector developed by Viola and Jones (VJ), that has been designed for gray-scale images is combined with a skin-color filter, which provides complementary information in color images. The image is first passed through a Haar-Feature based face detector, which is adjusted such that it is operating at a point on its ROC curve that has a low number of missed faces but a high number of false detections. Then, using the proposed skin color post-filtering method many of these false detections can be eliminated easily. We also use a color compensation algorithm to reduce the effects of lighting. Our experimental results on the Bao color face database show that the proposed method is superior to the original VJ algorithm and also to other skin color based pre-filtering methods in the literature in terms of precision. © 2011 IEEE. © 2011 Elsevier B.V., All rights reserved.Publication Metadata only RANSAC-based Training Data Selection for speaker state recognition(2011) Bozkurt, Elif; Erzin, Engin; Erdem, Cigdem Eroglu; Erdem, Tanju Tanju; Bozkurt, Elif, Vision and Graphics Laboratory, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, Vision and Graphics Laboratory, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Department of Electrical & Computer Engineering, Özyeğin Üniversitesi, Istanbul, TurkeyWe present a Random Sampling Consensus (RANSAC) based training approach for the problem of speaker state recognition from spontaneous speech. Our system is trained and tested with the INTERSPEECH 2011 Speaker State Challenge corpora that includes the Intoxication and the Sleepiness Sub-challenges, where each sub-challenge defines a two-class classification task. We aim to perform a RANSAC-based training data selection coupled with the Support Vector Machine (SVM) based classification to prune possible outliers, which exist in the training data. Our experimental evaluations indicate that utilization of RANSAC-based training data selection provides 66.32 % and 65.38 % unweighted average (UA) recall rate on the development and test sets for the Sleepiness Sub-challenge, respectively and a slight improvement on the Intoxication Sub-challenge performance. Copyright © 2011 ISCA. © 2012 Elsevier B.V., All rights reserved.Publication Metadata only RANSAC-based training data selection on spectral features for emotion recognition from spontaneous speech(2011) Bozkurt, Elif; Erzin, Engin; Erdem, Cigdem Eroglu; Erdem, Tanju Tanju; Bozkurt, Elif, College of Engineering, Koç Üniversitesi, Istanbul, Turkey; Erzin, Engin, College of Engineering, Koç Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Department of Electrical and Electronic Engineering, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Department of Electrical and Electronic Engineering, Özyeğin Üniversitesi, Istanbul, TurkeyTraining datasets containing spontaneous emotional speech are often imperfect due the ambiguities and difficulties of labeling such data by human observers. In this paper, we present a Random Sampling Consensus (RANSAC) based training approach for the problem of emotion recognition from spontaneous speech recordings. Our motivation is to insert a data cleaning process to the training phase of the Hidden Markov Models (HMMs) for the purpose of removing some suspicious instances of labels that may exist in the training dataset. Our experiments using HMMs with Mel Frequency Cepstral Coefficients (MFCC) and Line Spectral Frequency (LSF) features indicate that utilization of RANSAC in the training phase provides an improvement in the unweighted recall rates on the test set. Experimental studies performed over the FAU Aibo Emotion Corpus demonstrate that decision fusion configurations with LSF and MFCC based classifiers provide further significant performance improvements. © 2011 Springer-Verlag. © 2011 Elsevier B.V., All rights reserved.Publication Unknown Feature extraction for facial expression recognition by canonical correlation analysis, Kanoni̇k korelasyon anali̇zi̇ i̇le yüz i̇fadesi̇nden duygu tanima i̇çi̇n özni̇teli̇ k çikarimi(2012) Sakar, C. Okan; Kursun, Olcay; Karaali, Ali; Erdem, Cigdem Eroglu; Sakar, C. Okan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kursun, Olcay, Istanbul Üniversitesi, Istanbul, Turkey; Karaali, Ali, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyAlthough several methods have been proposed for fusing different image representations obtained by different preprocessing methods for emotion recognition from the facial expression in a given image, the dependencies and relations among them have not been much investigated. In this study, it has been shown that covariates obtained by Canonical Correlation Analysis (CCA) that extracts relations between different representations have high predictive power for emotion recognition. As high prediction accuracy can be achieved using a small number of features extracted by it, CCA is considered to be a good dimensionality reduction method. For our simulations, we used the CK+ database and showed that covariates obtained from difference-images and geometric-features representations have high prediction accuracy. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.Publication Unknown A hybrid facial expression recognition method based on neutral face shape estimation, Yüz i̇fadesi̇ tanima i̇çi̇n nötr yüz şekli̇ni̇n kesti̇ri̇lmesi̇ne dayali hi̇bri̇t bi̇r yöntem(2012) Ulukaya, Sezer; Erdem, Cigdem Eroglu; Ulukaya, Sezer, Boğaziçi Üniversitesi, Bebek, Turkey, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyIn order to recognize the facial expression of a person, the knowledge of the neutral facial expression of that person is useful but may not always be available.We present a method based on Gaussian mixture models (GMM) to estimate the unknown neutral facial expression of an expressive face. The estimated neutral face is then subtracted from the features of the expressive image and classified using support vector classifiers (SVC). Experimental results on the extended Cohn-Kanade (CK+) database give an emotion recognition rate of 88% using geometric features only and 92% if appearance based features are also included. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.Publication Unknown Inertial sensor fusion for 3D camera tracking, 3B kamera taki̇bi̇ i̇çi̇n eylemsi̇zli̇k algilayicilarinin bi̇rleşti̇ri̇lmesi̇(2012) Özer, Nuri; Erdem, Tanju Tanju; Ercan, Ali Özer; Erdem, Cigdem Eroglu; Özer, Nuri, Bahçeşehir Üniversitesi, Istanbul, Turkey; Erdem, Tanju Tanju, Özyeğin Üniversitesi, Istanbul, Turkey; Ercan, Ali Özer, Özyeğin Üniversitesi, Istanbul, Turkey; Erdem, Cigdem Eroglu, Bahçeşehir Üniversitesi, Istanbul, TurkeyIt is well known in a Bayesian filtering framework, the use of inertial sensors such as accelerometers and gyroscopes improves 3D tracking performance compared to using camera measurements only. The performance improvement is more evident when the camera undergoes a high degree of motion. However, it is not well known whether the inertial sensors should be used as control inputs or as measurements. In this paper, we present the results of an extensive set of simulations comparing different combinations of using inertial sensors as control inputs or as measurements. We show that it is better use a gyroscope as a control input while an accelerometer can be used as a measurement or control input. We also derive and present the extended Kalman filter (EKF) equations for a specific case of fusing accelerometer and gyroscope data that has not been reported before. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.