Vision-Based Dynamic Hand Gesture Recognition Techniques and Applications: A Review

Franslin, Nurfazlin Muhamad Feizal; Ng, Giap Weng

doi:10.1007/978-981-16-8515-6_11

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Hand gesture recognition is often divided into two categories [4,[15][16][17][18][19][20][21][22][23]: (1) Rule-based techniques, which include a set of rules that are manually configured among feature inputs. The rule that balances the input is output as the gesture after a set of features are retrieved and matched to the encoded rules.…”

Section: State Of the Artmentioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

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Section: State Of the Artmentioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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“…Gestures are a form of body language that offer an effective way of communicating with others. People use a variety of gestures ranging from simple ones (such as pointing and pinching) to more complex actions for expressing feelings [90]. In recent years, the maturity of gesture-recognition technology based on depth vision has promoted the development of gesture interaction technology in applications of CAD [91].…”

Section: Gesture Recognition-based Interaction For Cadmentioning

confidence: 99%

Multimodal Natural Human–Computer Interfaces for Computer-Aided Design: A Review Paper

et al. 2022

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Computer-aided design (CAD) systems have advanced to become a critical tool in product design. Nevertheless, they still primarily rely on the traditional mouse and keyboard interface. This limits the naturalness and intuitiveness of the 3D modeling process. Recently, a multimodal human–computer interface (HCI) has been proposed as the next-generation interaction paradigm. Widening the use of a multimodal HCI provides new opportunities for realizing natural interactions in 3D modeling. In this study, we conducted a literature review of a multimodal HCI for CAD to summarize the state-of-the-art research and establish a solid foundation for future research. We explore and categorize the requirements for natural HCIs and discuss paradigms for their implementation in CAD. Following this, factors to evaluate the system performance and user experience of a natural HCI are summarized and analyzed. We conclude by discussing challenges and key research directions for a natural HCI in product design to inspire future studies.

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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

Maidment,

Patel,

Walker

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Vision-Based Dynamic Hand Gesture Recognition Techniques and Applications: A Review

Cited by 4 publications

References 41 publications

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Multimodal Natural Human–Computer Interfaces for Computer-Aided Design: A Review Paper

Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

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