TUM – Technical University of Munich Menü

Prof. Dr. Gerhard Rigoll

Academic Career and Research Areas

The research of Prof. Rigoll (b. 1958) deals with all aspects of pattern recognition for multimodal human-machine interaction. Subfields include speech processing, audiovisual information processing, handwriting recognition, gesture and emotion recognition, face detection and recognition, object tracking and interactive graphical systems. He is the author or co-author of over 500 publications and has been member of many different programme committees. He has been involved in numerous expert panels in Germany and internationally.

After studying technical cybernetics in Stuttgart, he became research assistant at Fraunhofer Institute (IAO) in Stuttgart. He obtained his doctoral degree in 1986 with a thesis on automatic speech recognition. After that, he was a postdoctoral fellow at IBM Thomas Watson Research Center in Yorktown Heights/USA until 1988. After qualifying as a lecturer in Stuttgart from 1991 to 1993, he was a visiting scientist at the NTT Human Interface Laboratory in Tokyo. From 1993 to 2001, he was professor of computer engineering at Gerhard Mercator University in Duisburg prior to accepting his current position at TUM in 2002.

Awards

  • IEEE Fellow for Contributions to Multimodal Human-Machine Communication (2019)
  • DAGM-Award of the German Association for Pattern Recognition (2000)
  • Heisenberg-stipend from the German National Science Foundation (DFG) (1993)
  • FpF-Award for the best dissertation of the Stuttgart Fraunhofer-Institutes (1987)

Key Publications

Hofmann M, Tiefenbacher P, Rigoll G: "Background Segmentation with Feedback: The Pixel-Based Adaptive Segmenter".  2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, RI, USA. 16-21 June 2012.

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Hofmann M, Geiger J, Bachmann S, Schuller B, Rigoll G: "The TUM gait from audio, image and depth (gaid) database: Multimodal recognition of subjects and traits". Journal of Visual Communication and Image Representation. 2014; 25(1): 195-206.

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Wöllmer M, Kaiser M, Eyben F, Schuller B, Rigoll G: "LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework". Image and Vision Computing. 2013; 31(2): 153-163.

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Eickeler S, Müller S, Rigoll G: “Recognition of JPEG Compressed Face Images Based on Statistical Methods”. Image and Vision Computing Journal, Special Issue on Facial Image Analysis. 2000; 18(4): 279-287.

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Rigoll G: “Maximum Mutual Information Neural Networks for Hybrid Connectionist-HMM Speech Recognition Systems”. IEEE Transactions on Speech and Audio Processing, Special Issue on Neural Networks for Speech Processing. 1994; 2(1): 175-184.

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