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Descripción
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| Visual hand-gesture recognition is being increasingly desired for human-computer interaction interfaces. In many applications, hands only occupy about 10% of the image, whereas the most of it contains background, human face, and human body. Spatial localization of the hands in such scenarios could be a challenging task and ground truth bounding boxes need to be provided for training, which is usually not accessible. However, the location of the hand is not a requirement when the criteria is just the recognition of a gesture to command a consumer electronics device, such as mobiles phones and TVs. In this paper, a deep convolutional neural network is proposed to directly classify hand gestures in images without any segmentation or detection stage that could discard the irrelevant not-hand areas. The designed hand-gesture recognition network can classify seven sorts of hand gestures in a user-independent manner and on real time, achieving an accuracy of 97.1% in the dataset with simple backgrounds and 85.3% in the dataset with complex backgrounds. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Ieee Transactions on Consumer Electronics |
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ISSN
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0098-3063 |
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Factor de impacto JCR
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1,694 |
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Información de impacto
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Datos JCR del año 2016 |
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Volumen
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63 |
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DOI
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Número de revista
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3 |
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Desde la página
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251 |
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Hasta la página
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257 |
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Mes
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AGOSTO |
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Ranking
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