Autor/Autorin: Ari Ismail
Ismail Ari is a PhD student at Bogazici University, Turkey, where he received his BSc and MSc degrees in 2006 and 2008, respectively. Lale Akarun has been a full professor at Computer Engineering Department of Bogazici University since 2002. Besides guiding many dissertations, she has published many professional articles in refereed journals.
Autor/Autorin: Akarun Lale
Ismail Ari is a PhD student at Bogazici University, Turkey, where he received his BSc and MSc degrees in 2006 and 2008, respectively. Lale Akarun has been a full professor at Computer Engineering Department of Bogazici University since 2002. Besides guiding many dissertations, she has published many professional articles in refereed journals.
The focus of this work is on classifying the most common non-manual (facial) gestures in Sign Language. This goal is achieved in two consecutive steps: First, automatic facial landmarking is performed based on Multi-resolution Active Shape Models (MRASMs). Second, the tracked landmarks are normalized and expression classification is done based on multivariate Continuous Hidden Markov Model (CHMMs). We collected a video database of expressions from Turkish Sign Language (TSL) to test the proposed approach. The expressions used are universal and the results are applicable to other sign languages. Single view vs. multi-view and person specific vs. generic MRASM trackers are compared both for tracking and expression recognition. The multi-view person-specific tracker performs the best and tracks the landmarks robustly. For expression classification, the proposed CHMM classifier is tested on different training and test set combinations and the results are reported. We observe that the classification performances of distinct classes are very high.
The focus of this work is on classifying the most common non-manual (facial) gestures in Sign Language. This goal is achieved in two consecutive steps: First, automatic facial landmarking is performed based on Multi-resolution Active Shape Models (MRASMs). Second, the tracked landmarks are normalized and expression classification is done based on multivariate Continuous Hidden Markov Model (CHMMs). We collected a video database of expressions from Turkish Sign Language (TSL) to test the proposed approach. The expressions used are universal and the results are applicable to other sign languages. Single view vs. multi-view and person specific vs. generic MRASM trackers are compared both for tracking and expression recognition. The multi-view person-specific tracker performs the best and tracks the landmarks robustly. For expression classification, the proposed CHMM classifier is tested on different training and test set combinations and the results are reported. We observe that the classification performances of distinct classes are very high.
smail Ar is a PhD student at Bogazici University, Turkey, where he received his BSc and MSc degrees in 2006 and 2008, respectively. Lale Akarun has been a full professor at Computer Engineering Department of Bogazici University since 2002. Besides guiding many dissertations, she has published many professional articles in refereed journals.
Über den Autor
¿smail Ar¿ is a PhD student at Bogazici University, Turkey, where he received his BSc and MSc degrees in 2006 and 2008, respectively. Lale Akarun has been a full professor at Computer Engineering Department of Bogazici University since 2002. Besides guiding many dissertations, she has published many professional articles in refereed journals.
Klappentext
The focus of this work is on classifying the most common non-manual (facial) gestures in Sign Language. This goal is achieved in two consecutive steps: First, automatic facial landmarking is performed based on Multi-resolution Active Shape Models (MRASMs). Second, the tracked landmarks are normalized and expression classification is done based on multivariate Continuous Hidden Markov Model (CHMMs). We collected a video database of expressions from Turkish Sign Language (TSL) to test the proposed approach. The expressions used are universal and the results are applicable to other sign languages. Single view vs. multi-view and person specific vs. generic MRASM trackers are compared both for tracking and expression recognition. The multi-view person-specific tracker performs the best and tracks the landmarks robustly. For expression classification, the proposed CHMM classifier is tested on different training and test set combinations and the results are reported. We observe that the classification performances of distinct classes are very high.
