Finger-spelling recognition system using fuzzy finger shape and hand appearance features

Silanon, Kittasil; Suvonvorn, Nikom

doi:10.1109/dictap.2014.6821723

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Generally, there are three levels of sign language recognition: finger spelling (alphabets), isolated sign gestures and continuous sign gestures. For finger spelling, the number of hand motions is very small and sometimes mainly finger configuration and orientation information are included [ 30 , 31 , 32 ]. For isolated sign gestures, usually features that characterize whole hand location and movement as well as appearance features that result from hand shape and orientation are extracted [ 3 , 13 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors

Cheng

Chen

Liu

et al. 2015

Sensors

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Sign language recognition (SLR) is an important communication tool between the deaf and the external world. It is highly necessary to develop a worldwide continuous and large-vocabulary-scale SLR system for practical usage. In this paper, we propose a novel phonology- and radical-coded Chinese SLR framework to demonstrate the feasibility of continuous SLR using accelerometer (ACC) and surface electromyography (sEMG) sensors. The continuous Chinese characters, consisting of coded sign gestures, are first segmented into active segments using EMG signals by means of moving average algorithm. Then, features of each component are extracted from both ACC and sEMG signals of active segments (i.e., palm orientation represented by the mean and variance of ACC signals, hand movement represented by the fixed-point ACC sequence, and hand shape represented by both the mean absolute value (MAV) and autoregressive model coefficients (ARs)). Afterwards, palm orientation is first classified, distinguishing “Palm Downward” sign gestures from “Palm Inward” ones. Only the “Palm Inward” gestures are sent for further hand movement and hand shape recognition by dynamic time warping (DTW) algorithm and hidden Markov models (HMM) respectively. Finally, component recognition results are integrated to identify one certain coded gesture. Experimental results demonstrate that the proposed SLR framework with a vocabulary scale of 223 characters can achieve an averaged recognition accuracy of 96.01% ± 0.83% for coded gesture recognition tasks and 92.73% ± 1.47% for character recognition tasks. Besides, it demonstrats that sEMG signals are rather consistent for a given hand shape independent of hand movements. Hence, the number of training samples will not be significantly increased when the vocabulary scale increases, since not only the number of the completely new proposed coded gestures is constant and limited, but also the transition movement which connects successive signs needs no training samples to model even though the same coded gesture performed in different characters. This work opens up a possible new way to realize a practical Chinese SLR system.

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Section: Introductionmentioning

confidence: 99%

A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography Sensors

Cheng

Chen

Liu

et al. 2015

Sensors

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“…Contour tracking uses deformable contours or snakes to track regions over consecutive frames. For example, a nger spelling detector is developed using this technique in [13] .…”

Section: Trackingmentioning

confidence: 99%

“…13 shows the confusion matrix. Looking at the main diagonal of that table, it can be observed that the performance of many of the gestures is more than an 80% indicating also a stable performance.…”

mentioning

confidence: 99%