Automatic hand gesture recognition using hybrid meta-heuristic-based feature selection and classification with Dynamic Time Warping

Kowdiki, Manisha; Khaparde, Arti

doi:10.1016/j.cosrev.2020.100320

Cited by 22 publications

(9 citation statements)

References 29 publications

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“…Several works exploring this track have been already proposed (e.g. Seger, Wanderley, and Koerich, 2014, Schneider, Memmesheimer, Kramer, and Paulus, 2019, Kowdiki and Khaparde, 2021.…”

Section: Discussionmentioning

confidence: 99%

Automatic tool to annotate smile intensities in conversational face-to-face interactions

Rauzy,

Amoyal

2022

GEST

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This study presents an automatic tool that allows to trace smile intensities along a video record of conversational face-to-face interactions. The processed output proposes a sequence of adjusted time intervals labeled following the Smiling Intensity Scale (Gironzetti, Attardo, and Pickering, 2016), a 5 levels scale varying from neutral facial expression to laughing smile. The underlying statistical model of this tool is trained on a manually annotated corpus of conversations featuring spontaneous facial expressions. This model will be detailed in this study. This tool can be used with benefits for annotating smile in interactions. The results are twofold. First, the evaluation reveals an observed agreement of 68% between manual and automatic annotations. Second, manually correcting the labels and interval boundaries of the automatic outputs reduces by a factor 10 the annotation time as compared with the time spent for manually annotating smile intensities without pretreatment. Our annotation engine makes use of the state-of-the-art toolbox OpenFace for tracking the face and for measuring the intensities of the facial Action Units of interest all along the video. The documentation and the scripts of our tool, the SMAD software, are available to download at the HMAD open source project URL page https://github.com/srauzy/HMAD (last access 31 July 2023).

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“…Several works exploring this track have been already proposed (e.g. Seger, Wanderley, and Koerich, 2014, Schneider, Memmesheimer, Kramer, and Paulus, 2019, Kowdiki and Khaparde, 2021.…”

Section: Discussionmentioning

confidence: 99%

Automatic tool to annotate smile intensities in conversational face-to-face interactions

Rauzy,

Amoyal

2022

GEST

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“…The following three methods are the most common ones that may produce features: viewpoint, model (Kinematic Model), and appearance-based low-level feature approaches. Refer to [47][48][49][50] for a comprehensive overview of the use of gesture feature extraction for static gesture recognition. In order to improve the accuracy of the hand posture identification scheme and to circumvent issues caused by multiple modifications like rotating, resizing, and reallocating, the SVD feature extraction method is applied to the skeleton of the hand shape using the fewest number of pixels without losing the contour data in order to identify unique and distinguishable features [50].…”

Section: Feature Extractionmentioning

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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“…Numerous applications have benefted from the DTW algorithm; those applications include increasing the accuracy of connected digit recognition [29], which creates a reference string by concatenating the reference contours of the digits and comparing it to the test string. Selecting the gesture candidates for recognizing real-time hand gestures of a Kinect sensor is another application of the dynamic time warping [30,31]. Additional uses involve identifying Alzheimer disease by comparing foot movements [32,33], fnding the imputation of missing values for univariate time series data [34], and diferentiating bee propolis based on its geographical origin [35].…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform

Fathalla

Salah

Bekhit

et al. 2023

International Journal of Intelligent Systems

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In sports science, the automation of performance analysis and assessment is urgently required to increase the evaluation accuracy and decrease the performance analysis time of a subject. Existing methods of performance analysis and assessment are either performed manually based on human experts’ opinions or using motion analysis software, i.e., biomechanical analysis software, to assess only one side of a subject. Therefore, we propose an automated system for performance analysis and assessment that can be used for any human movement. The performance of any skill can be described by a curve depicting the joint angle over the time required to perform a skill. In this study, we focus on only 14 body joints, and each joint comprises three angles. The proposed system comprises three main stages. In the first stage, data are obtained using motion capture inertial measurement unit sensors from top professional fighters/players while they are performing a certain skill. In the second stage, the collected sensor data obtained are input to the biomechanical software to extract the player’s joint angle curve. Finally, each joint angle curve is processed using a continuous wavelet transform to extract the main curve points (i.e., peaks and valleys). Finally, after extracting the joint curves from several top players, we summarize the players’ curves based on five statistical indicators, i.e., the minimum, maximum, mean, and mean ± standard deviation. These five summarized curves are regarded as standard performance curves for the joint angle. When a player’s joint curve is surrounded by the five summarized curves, the performance is considered acceptable. Otherwise, the performance is considered unsatisfactory. The proposed system is evaluated based on four different karate skills. The results of the proposed system are identical to the decisions of the expert panels and are thus suitable for real-time decisions.

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Automatic hand gesture recognition using hybrid meta-heuristic-based feature selection and classification with Dynamic Time Warping

Cited by 22 publications

References 29 publications

Automatic tool to annotate smile intensities in conversational face-to-face interactions

Automatic tool to annotate smile intensities in conversational face-to-face interactions

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

Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform

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