By Professor Roland Goecke

Conference Papers

Khwaja AA; Goecke R, 2009, 'Biologically inspired contrast enhancement using asymmetric gain control', in DICTA 2009 - Digital Image Computing: Techniques and Applications, pp. 424 - 430, http://dx.doi.org/10.1109/DICTA.2009.75

Asthana A; Saragih J; Wagner M; Goecke R, 2009, 'Evaluating AAM fitting methods for facial expression recognition', in Proceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009, http://dx.doi.org/10.1109/ACII.2009.5349489

Zhu D; Mendis BSU; Gedeon T; Asthana A; Goecke R, 2009, 'A hybrid fuzzy approach for human eye gaze pattern recognition', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 655 - 662, http://dx.doi.org/10.1007/978-3-642-03040-6_80

Chetty G; Göcke R; Wagner M, 2009, 'Audio-Visual Mutual Dependency Models for Biometric Liveness Checks', in Auditory-Visual Speech Processing 2009, AVSP 2009, pp. 32 - 37

Shochi T; Sekiyama K; Lees N; Boyce M; Göcke R; Burnham D, 2009, 'Auditory-Visual Infant Directed Speech in Japanese and English', in Auditory-Visual Speech Processing 2009, AVSP 2009, pp. 107 - 112

Asthana A; Khwaja A; Goecke R, 2009, 'Automatic frontal face annotation and AAM building for arbitrary expressions from a single frontal image only', in Proceedings - International Conference on Image Processing, ICIP, pp. 2445 - 2448, http://dx.doi.org/10.1109/ICIP.2009.5414123

Asthana A; Goecke R; Quadrianto N; Gedeon T, 2009, 'Learning based automatic face annotation for arbitrary poses and expressions from frontal images only', in 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1635 - 1642, http://dx.doi.org/10.1109/CVPRW.2009.5206766

Asthana A; Sanderson C; Gedeon T; Goecke R, 2009, 'Learning-based face synthesis for pose-robust recognition from single image', in British Machine Vision Conference, BMVC 2009 - Proceedings, http://dx.doi.org/10.5244/C.23.31

McIntyre G; Goecke R, 2008, 'A composite framework for affective sensing', in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, pp. 2767 - 2770

Khwaja AA; Goecke R, 2008, 'Image reconstruction from contrast information', in Proceedings - Digital Image Computing: Techniques and Applications, DICTA 2008, pp. 226 - 233, http://dx.doi.org/10.1109/DICTA.2008.66

McIntyre G; Göcke R, 2008, 'The composite sensing of affect', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 104 - 115, http://dx.doi.org/10.1007/978-3-540-85099-1_9

Huy TH; Goecke R, 2008, 'Optical flow estimation using fourier mellin transform', in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, http://dx.doi.org/10.1109/CVPR.2008.4587553

Göcke R; Asthana A, 2008, 'A Comparative Study of 2D and 3D Lip Tracking Methods for AV ASR', in International Conference on Auditory-Visual Speech Processing 2008, AVSP 2008, pp. 235 - 240

Saragih J; Goecke R, 2007, 'A nonlinear discriminative approach to AAM fitting', in Proceedings of the IEEE International Conference on Computer Vision, http://dx.doi.org/10.1109/ICCV.2007.4409106

Goecke R; Ho HT, 2007, 'Quaternion potential functions for a colour image completion method using Markov Random Fields', in Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007, pp. 324 - 331, http://dx.doi.org/10.1109/DICTA.2007.4426814

Saragih J; Goecke R, 2007, 'Monocular and stereo methods for AAM learning from video', in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, http://dx.doi.org/10.1109/CVPR.2007.383058

Peter C; Schultz R; Voskamp J; Urban B; Nowack N; Janik H; Kraft K; Göcke R, 2007, 'EREC-II in use - studies on usability and suitability of a sensor system for affect detection and human performance monitoring', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 465 - 474, http://dx.doi.org/10.1007/978-3-540-73110-8_50

McIntyre G; Göcke R, 2007, 'Towards affective sensing', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 411 - 420, http://dx.doi.org/10.1007/978-3-540-73110-8_44

Goecke R; Pettersson N; Petersson L, 2007, 'Towards detection and tracking of on-road objects', in IEEE Intelligent Vehicles Symposium, Proceedings, pp. 416 - 421, http://dx.doi.org/10.1109/ivs.2007.4290150

Goecke R; Asthana A; Pettersson N; Petersson L, 2007, 'Visual vehicle egomotion estimation using the Fourier-Mellin Transform', in IEEE Intelligent Vehicles Symposium, Proceedings, pp. 450 - 455, http://dx.doi.org/10.1109/ivs.2007.4290156

Robles-Kelly A; Goecke R, 2006, 'A wavelet-based approach to image feature stability assessment', in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, http://dx.doi.org/10.1109/CVPRW.2006.22

Huy TH; Goecke R, 2006, 'Automatic parametrisation for an image completion method based on markov random fields', in Proceedings - International Conference on Image Processing, ICIP, http://dx.doi.org/10.1109/ICIP.2007.4379366

Saragih J; Goecke R, 2006, 'Iterative error bound minimisation for AAM alignment', in Proceedings - International Conference on Pattern Recognition, pp. 1195 - 1196, http://dx.doi.org/10.1109/ICPR.2006.730

Goecke R, 2005, 'Current trends in joint audio-video signal processing: A review', in Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005, pp. 70 - 73, http://dx.doi.org/10.1109/ISSPA.2005.1580198

Goecke R, 2005, '3D LIP TRACKING AND CO-INERTIA ANALYSIS FOR IMPROVED ROBUSTNESS OF AUDIO-VIDEO AUTOMATIC SPEECH RECOGNITION', in Auditory-Visual Speech Processing 2005, AVSP 2005, pp. 109 - 114

Mader S; Peter C; Gocke R; Schultz R; Voskamp J; Urban B, 2004, 'A freely configurable, multi-modal sensor system for affective computing', in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), pp. 313 - 318, http://dx.doi.org/10.1007/978-3-540-24842-2_34

Millar JB; Wagner M; Goecke R, 2004, 'Aspects of speaking-face data corpus design methodology', in 8th International Conference on Spoken Language Processing, ICSLP 2004, pp. 1157 - 1160

Goecke R; Millar JB, 2004, 'The audio-video australian english speech data corpus AVOZES', in 8th International Conference on Spoken Language Processing, ICSLP 2004, pp. 2525 - 2528

Göcke R, 2003, 'Building a system for writer identification on handwritten music scores', in Proceedings of the IASTED International Conference on Signal Processing, Pattern Reconition, and Applications, pp. 250 - 255

Goecke R; Millar B, 2003, 'Statistical Analysis of the Relationship between Audio and Video Speech Parameters for Australian English', in AVSP 2003 - International Conference on Audio-Visual Speech Processing, pp. 133 - 138

Goecke R; Millar JB; Zelinsky A; Robert-Ribes J, 2001, 'ANALYSIS OF AUDIO-VIDEO CORRELATION IN VOWELS IN AUSTRALIAN ENGLISH', in Auditory-Visual Speech Processing 2001, AVSP 2001, pp. 115 - 120

Preprints

Narayana S; Radwan I; Subramanian R; Goecke R, 2024, Mood as a Contextual Cue for Improved Emotion Inference, http://arxiv.org/abs/2402.08413v1

Parameshwara R; Radwan I; Asthana A; Abbasnejad I; Subramanian R; Goecke R, 2023, Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning, http://arxiv.org/abs/2308.02173v1

Gahalawat M; Rojas RF; Guha T; Subramanian R; Goecke R, 2023, Explainable Depression Detection via Head Motion Patterns, http://arxiv.org/abs/2307.12241v1

Narayana S; Radwan I; Parameshwara R; Abbasnejad I; Asthana A; Subramanian R; Goecke R, 2023, A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference, http://arxiv.org/abs/2306.06979v2

Narayana S; Subramanian R; Radwan I; Goecke R, 2023, Focus on Change: Mood Prediction by Learning Emotion Changes via Spatio-Temporal Attention, http://arxiv.org/abs/2303.06632v1

Madan S; Gahalawat M; Guha T; Goecke R; Subramanian R, 2023, Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics, http://arxiv.org/abs/2302.09817v2

Narayana S; Subramanian R; Radwan I; Goecke R, 2022, To Improve Is to Change: Towards Improving Mood Prediction by Learning Changes in Emotion, http://arxiv.org/abs/2210.00719v1

Narayana S; Jain S; Katti H; Goecke R; Subramanian R, 2022, Affective Computational Advertising Based on Perceptual Metrics, http://arxiv.org/abs/2207.07297v1

Parameshwara R; Narayana S; Murugappan M; Subramanian R; Radwan I; Goecke R, 2022, Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals, http://arxiv.org/abs/2202.12936v1

Malik H; Dhillon H; Goecke R; Subramanian R, 2020, Characterizing Hirability via Personality and Behavior, http://arxiv.org/abs/2006.12041v1

Mustafa A; Khan S; Hayat M; Goecke R; Shen J; Shao L, 2019, Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, http://arxiv.org/abs/1904.00887v4

Huang X; Dhall A; Goecke R; Pietikainen M; Zhao G, 2018, A Global Alignment Kernel based Approach for Group-level Happiness Intensity Estimation, http://arxiv.org/abs/1809.03313v1

Dhall A; Kaur A; Goecke R; Gedeon T, 2018, EmotiW 2018: Audio-Video, Student Engagement and Group-Level Affect Prediction, http://arxiv.org/abs/1808.07773v1

Huang X; Dhall A; Liu X; Zhao G; Shi J; Goecke R; Pietikainen M, 2016, Analyzing the Affect of a Group of People Using Multi-modal Framework, http://arxiv.org/abs/1610.03640v2

Murthy OVR; Goecke R, 2015, Harnessing the Deep Net Object Models for Enhancing Human Action Recognition, http://arxiv.org/abs/1512.06498v2

Radwan I; Dhall A; Goecke R, 2015, Occlusion-Aware Human Pose Estimation with Mixtures of Sub-Trees, http://arxiv.org/abs/1512.01055v1

Hassanin M; radwan I; Tahtali M; Goecke R, Resanet: Residual Aggregation Networks for Dense Prediction, http://dx.doi.org/10.2139/ssrn.4212324

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