Christopher Middendorff

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3ranked-venue papers

1as first author

0since 2021 · last 2007

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing 2 papers		Human-AI interaction · 65% Usability and user experience research · 35%
Network and information security 1 paper		Biometric security · 100%
Artificial intelligence 2 papers		Robot navigation and mapping · 54% Face, body and person analysis · 46%

Topics — the 6 heaviest of 7, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Biometric security ear biometrics	0.1	1	2007	Multi-Modal Biometrics Involving the Human Ear · CVPR 2007
Biometric security › biometric fusion multimodal biometric fusion	0.1	1	2007	Multi-Modal Biometrics Involving the Human Ear · CVPR 2007
Human-AI interaction › large language model interaction › language-based interaction natural language interface	0.1	1	2006	DIARC: A Testbed for Natural Human-Robot Interaction · AAAI 2006
Usability and user experience research testbed	0.1	1	2006	DIARC: A Testbed for Natural Human-Robot Interaction · AAAI 2006
Computer vision › Face, body and person analysis › affective computing emotion modeling	0.1	1	2005	Toward Affective Cognitive Robots for Human-Robot Interaction · AAAI 2005
Human-AI interaction affective computing	0.1	1	2005	Toward Affective Cognitive Robots for Human-Robot Interaction · AAAI 2005

Year	Publication	Venue	Position
2007	Multi-Modal Biometrics Involving the Human Ear abstract Due to its semi-rigid shape and robustness against change over time, the ear has become an increasingly popular biometric feature. It has been shown that combining individual biometric methods into multi-biometric systems improves recognition. What features should be used, how they should be captured, what algorithms should be used, and how they should be combined are all open questions. In this paper, we discuss several existing methods of combination and the recognition rates of each. Christopher Middendorff, Kevin W. Bowyer	CVPR	1
2006	DIARC: A Testbed for Natural Human-Robot Interaction Paul W. Schermerhorn, James F. Kramer, Christopher Middendorff, Matthias Scheutz	AAAI	3
2005	Toward Affective Cognitive Robots for Human-Robot Interaction Matthias Scheutz, James F. Kramer, Christopher Middendorff, Paul W. Schermerhorn, Michael Heilman, P. Bui	AAAI	3