Feature Extraction from Smartphone Images by Using Elliptical Fourier Descriptor, Centroid and Area for Recognizing Indonesian Sign Language SIBI (Sistem Isyarat Bahasa Indonesia)

Fauzan, Mohamad Harits Nur; Rakun, Erdefi; Hardianto, Dadan

doi:10.1109/icoias.2019.00008

Cited by 5 publications

(2 citation statements)

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“…The 4 th phase of the research initiative involves the separation of gestures and epenthesis movements. In a previous study, Widhinugraha and Rakun [23] conducted a segmentation process using a TCRF model on SIBI inflectional word gestures only. The TCRF model with a Gaussian variance of 50, over 500 iterations and a threshold of 1.5 achieved an accuracy of 81.5% in identifying epenthesis gestures in an inflectional words-only dataset, using data recorded from a Kinect sensor.…”

Section: Proposed Methods For Sibi Sentence Recognition Systemmentioning

confidence: 99%

Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures

Rakun

Widhinugraha

Setyono

2022

IJEECS

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This research <span>focuses on building a system to translate continuous Indonesian sign system (SIBI) gestures into text. In a continuous gesture, a signer will add an epenthesis (transitional) gesture, which is hand movement with no meaning but needed to connect the hand movement of one word with the next word in a continuous gesture. Reducing the number of irrelevant inputs to the model through automated epenthesis removal can improve the system's ability to recognize the words in continuous gestures. We implemented threshold conditional random fields (TCRF) to identify epenthesis gestures. The dataset consists of 2,255 videos representing 28 common sentences in SIBI. The translation system consists of MobileNetV2 as a feature extraction technique, removing epenthesis gestures found by the TCRF, and a long short-term memory (LSTM) for the classifier. With the MobileNetV2-TCRF-bidirectional LSTM model, the best word error rate (WER) and sentence accuracy (SAcc) were 33.4% and 16.2%, respectively. Intermediate-stage processing steps consisting of sandwiched majority voting of the TCRF and the removal of word labels whose number of frames is less than two frames, along with LSTM output grouping, were able to reduce WER from 33.4% to 3.4% and increase SAcc from 16.2% to 80.2%.</span>

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Section: Proposed Methods For Sibi Sentence Recognition Systemmentioning

confidence: 99%

Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures

Rakun

Widhinugraha

Setyono

2022

IJEECS

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“…Elliptic Fourier descriptors and hand features 2019Fauzan [32] Indian Sign Language: 99% (Root gestures), 71% (Prefix gestures)…”

Section: Fourier Filteringmentioning

confidence: 99%

Review of vision-based gesture recognition technology

bao,

dai,

2024

International Conference on Optics and Machine Vision (ICOMV 2024)

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With the emergence of deep learning methodologies, vision-based gesture recognition technology has continuously advanced. This paper primarily delves into four main stages of vision-based gesture recognition: gesture segmentation, gesture tracking, feature extraction, and gesture classification. It sequentially introduces pertinent techniques from representative literature spanning from 2018 to 2023. Based on this analysis, the current status of vision-based gesture recognition technology is examined, paving the way for predicting its future trends and developments.

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Recognizing Word Gesture in Sign System for Indonesian Language (SIBI) Sentences Using DeepCNN and BiLSTM

Setyono

Rakun

2019

2019 International Conference on Advanced Computer Science and Information Systems (ICACSIS)

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Feature Extraction from Smartphone Images by Using Elliptical Fourier Descriptor, Centroid and Area for Recognizing Indonesian Sign Language SIBI (Sistem Isyarat Bahasa Indonesia)

Cited by 5 publications

References 14 publications

Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures

Word recognition and automated epenthesis removal for Indonesian sign system sentence gestures

Review of vision-based gesture recognition technology

Recognizing Word Gesture in Sign System for Indonesian Language (SIBI) Sentences Using DeepCNN and BiLSTM

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