Persian sign language detection based on normalized depth image information

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Rajabi Shahab; Mousavinia Amir

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Journal: Journal of Control Year:2019 | Volume:13 | Issue:3 Page(s): 29-39

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Information Journal Paper

Title

Persian sign language detection based on normalized depth image information

Author(s)

Rajabi Shahab | Mousavinia Amir | Issue Writer Certificate

Keywords

Persian sign languageQ1

deaf peopleQ1

Kinect sensorQ2

waveletQ1

circular descriptorQ2

Abstract

There are many reports of using the Kinect to detect hand and finger gestures after release of device by Microsoft. The depth information is mostly used to separate the hand image in the two-dimension of RGB domain. This paper proposes a method in which the depth information plays a more dominant role. Using a threshold in depth space first the hand template is extracted. Then in 3D domain the perpendicular vector to the hand surface is found. Using the rotation matrix all the rotations along three axes are compensated in a way that the camera z-coordinate lies perpendicular to hand surface. Then the resulted 3d image is translated to a distance of 80 to 100 cm from the Kinect. wavelet transform with a new descriptor, called circular descriptor are used to extract required features. A trained MLP neural network in conjunction with a SVM is used to classify the signs. Empirical results show an average accuracy of 96. 7 % with a two seconds delay for online recognition of Persian sign language.

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APA: Copy

Rajabi, Shahab, & Mousavinia, Amir. (2019). Persian sign language detection based on normalized depth image information. JOURNAL OF CONTROL, 13(3 ), 29-39. SID. https://sid.ir/paper/120758/en

Vancouver: Copy

Rajabi Shahab, Mousavinia Amir. Persian sign language detection based on normalized depth image information. JOURNAL OF CONTROL[Internet]. 2019;13(3 ):29-39. Available from: https://sid.ir/paper/120758/en

IEEE: Copy

Shahab Rajabi, and Amir Mousavinia, “Persian sign language detection based on normalized depth image information,” JOURNAL OF CONTROL, vol. 13, no. 3 , pp. 29–39, 2019, [Online]. Available: https://sid.ir/paper/120758/en

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