A novel competitive neural classifier for gesture recognition with small training sets
Gesture recognition is a major area of interest in human-computer interaction. Recent advances in sensor technology and Computer power has allowed us to perform real-time joint tracking with com-modity hardware, but robust, adaptable, user-independent usable hand gesture classification remains an op...
Guardado en:
| Autores principales: | , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2013
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/31580 |
| Aporte de: |
| id |
I19-R120-10915-31580 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas gesture recognition scale invariant speed invariant starting point invariant neural network CPN competitive Neural nets Object recognition |
| spellingShingle |
Ciencias Informáticas gesture recognition scale invariant speed invariant starting point invariant neural network CPN competitive Neural nets Object recognition Quiroga, Facundo Corbalán, Leonardo César A novel competitive neural classifier for gesture recognition with small training sets |
| topic_facet |
Ciencias Informáticas gesture recognition scale invariant speed invariant starting point invariant neural network CPN competitive Neural nets Object recognition |
| description |
Gesture recognition is a major area of interest in human-computer interaction. Recent advances in sensor technology and Computer power has allowed us to perform real-time joint tracking with com-modity hardware, but robust, adaptable, user-independent usable hand gesture classification remains an open problem. Since it is desirable that users can record their own gestures to expand their gesture vocabulary, a method that performs well on small training sets is required. We propose a novel competitive neural classifier (CNC) that recognizes arabic numbers hand gestures with a 98% success rate, even when trained with a small sample set (3 gestures per class). The approach uses the direction of movement between gesture sampling points as features and is time, scale and translation invariant. By using a technique borrowed from ob-ject and speaker recognition methods, it is also starting-point invariant, a new property we define for closed gestures. We found its performance to be on par with standard classifiers for temporal pattern recognition. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Quiroga, Facundo Corbalán, Leonardo César |
| author_facet |
Quiroga, Facundo Corbalán, Leonardo César |
| author_sort |
Quiroga, Facundo |
| title |
A novel competitive neural classifier for gesture recognition with small training sets |
| title_short |
A novel competitive neural classifier for gesture recognition with small training sets |
| title_full |
A novel competitive neural classifier for gesture recognition with small training sets |
| title_fullStr |
A novel competitive neural classifier for gesture recognition with small training sets |
| title_full_unstemmed |
A novel competitive neural classifier for gesture recognition with small training sets |
| title_sort |
novel competitive neural classifier for gesture recognition with small training sets |
| publishDate |
2013 |
| url |
http://sedici.unlp.edu.ar/handle/10915/31580 |
| work_keys_str_mv |
AT quirogafacundo anovelcompetitiveneuralclassifierforgesturerecognitionwithsmalltrainingsets AT corbalanleonardocesar anovelcompetitiveneuralclassifierforgesturerecognitionwithsmalltrainingsets AT quirogafacundo novelcompetitiveneuralclassifierforgesturerecognitionwithsmalltrainingsets AT corbalanleonardocesar novelcompetitiveneuralclassifierforgesturerecognitionwithsmalltrainingsets |
| bdutipo_str |
Repositorios |
| _version_ |
1764820471339024387 |