Sign Languages of the World

Jepsen, Julie Bakken; Lutalo-Kiingi, Sam; McGregor, William B.; Clerck, Goedele De

doi:10.1515/9781614518174

Cited by 30 publications

(1 citation statement)

References 15 publications

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“…Table 1 shows the indicated ten continuous-time hand gesture action categorizations, each of which represents the specific semantic human intention behavior in the real life of laboratory environments. Note that each of these gesture actions operated is well-designed according to the standard sign language (specific sign language used in social actions of Taiwan) definition in [ 27 ]. These actions of Action-1, Action-2, …, and Action-10 are essentially common in an interaction scenario of a laboratory office composed of student members and the leading teacher, and very beneficial for the smart social activities of a normal group or smart person communication between the normal and the disabled, which are “To the restroom!,” “What?”, “Good bye!”, “Is it ok?”, “Good morning!”, “The teacher!”, “The exam!”, “Hello!”, “Calm down!”, and “Exchange!”, respectively.…”

Section: Methodsmentioning

confidence: 99%

CNN Deep Learning with Wavelet Image Fusion of CCD RGB-IR and Depth-Grayscale Sensor Data for Hand Gesture Intention Recognition

Ding

Zheng

2022

Sensors

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Pixel-based images captured by a charge-coupled device (CCD) with infrared (IR) LEDs around the image sensor are the well-known CCD Red–Green–Blue IR (the so-called CCD RGB-IR) data. The CCD RGB-IR data are generally acquired for video surveillance applications. Currently, CCD RGB-IR information has been further used to perform human gesture recognition on surveillance. Gesture recognition, including hand gesture intention recognition, is attracting great attention in the field of deep neural network (DNN) calculations. For further enhancing conventional CCD RGB-IR gesture recognition by DNN, this work proposes a deep learning framework for gesture recognition where a convolution neural network (CNN) incorporated with wavelet image fusion of CCD RGB-IR and additional depth-based depth-grayscale images (captured from depth sensors of the famous Microsoft Kinect device) is constructed for gesture intention recognition. In the proposed CNN with wavelet image fusion, a five-level discrete wavelet transformation (DWT) with three different wavelet decomposition merge strategies, namely, max-min, min-max and mean-mean, is employed; the visual geometry group (VGG)-16 CNN is used for deep learning and recognition of the wavelet fused gesture images. Experiments on the classifications of ten hand gesture intention actions (specified in a scenario of laboratory interactions) show that by additionally incorporating depth-grayscale data into CCD RGB-IR gesture recognition one will be able to further increase the averaged recognition accuracy to 83.88% for the VGG-16 CNN with min-max wavelet image fusion of the CCD RGB-IR and depth-grayscale data, which is obviously superior to the 75.33% of VGG-16 CNN with only CCD RGB-IR.

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Section: Methodsmentioning

confidence: 99%