Fusing Multimodal features for Recognizing Hand Gestures

Suni, S.S.; Gopakumar, K.

doi:10.1109/icaccp.2019.8882910

Cited by 3 publications

(5 citation statements)

References 14 publications

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“…This section describes the proposed technique's comparison results, in which our novel technique is compared to baseline approaches such as volumetric Spatiograms of either the Local Binary Pattern (VS-LBP) [45], Local Binary Pattern (LBP) [46], Temporal Pyramid Matching of the Local Binary Pattern (TPM-LBP) [47], Pyramid Histogram of Gradients (PHOG) [48], as well as Scale Invariant Feature Transform (SIFT) [49].…”

Section: Comparison Resultsmentioning

confidence: 99%

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Hand Gesture Recognizing Model Using Optimized Capsule Neural Network

Suni¹,

Gopakumar²

2022

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Hand gestures are a sort of nonverbal communication that may be utilized for many diverse purposes, including deaf-mute interaction, robotic manipulation, human-computer interface (HCI), residential management, and healthcare usage. Moreover, most current research uses the artificial intelligence approach effectively to extract dense features from hand gestures. Since most of them used neural network models, the performance of the models influences the modification of the hyperparameter to enhance recognition accuracy. Therefore, our research proposed a capsule neural network, in which the internal computations on the inputs are better encapsulated by transforming the findings into a tiny vector of information outputs. Moreover, to increase the accuracy of recognizing hand gestures, the neural network has been optimized by inserting additional SoftMax layers before the output layer of the CapsNet. Subsequently, the findings of the tests were assessed and then compared. This developed approach has been beneficial across all tests when contrasted against state-of-the-art systems.

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Section: Comparison Resultsmentioning

confidence: 99%

“…Accuracy (%) VS-LBP [45] 92.7 LBP [46] 91.5 TPM-LBP [47] 96.5 PHOG [48] 94.6 SIFT [49] 97.6 Proposed Method 99.5…”

Section: Methodsmentioning

confidence: 99%

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Hand Gesture Recognizing Model Using Optimized Capsule Neural Network

Suni¹,

Gopakumar²

2022

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“…The Temporal Pyramid Matching of Local Binary Pattern (TPM-LBP) algorithm created by authors of [33] achieved good recognition rate with the expense of computational complexity. In one of recent work [39], hand gesture recognition based on pyramid histogram of gradients (PHOG) and optical flow achieves a recognition rate of 94.6% with the expense of high dimensional feature vectors. From this analysis, we can found that proposed architecture shows considerable improvement in recognition rate and work well in non-uniform background and varying viewpoints.…”

Section: Comparative Analysismentioning

confidence: 99%

“…The features from contour shape (orientation, seven hu moments area and perimeter) and motion of hand (angle and velocity of movement) are used to detect hand gestures and achieved a better error rate 27.6% [38]. Another work [39] obtained 11% of error rate by using a multiple feature based framework. The proposed SIFT flow based architecture attained an error rate of 7%.…”

Section: Comparative Analysismentioning

confidence: 99%

Dense SIFT–Flow based Architecture for Recognizing Hand Gestures

Suni¹,

Gopakumar²

2020

Adv. sci. technol. eng. syst. j.

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Several challenges like changes in brightness, dynamic background, occlusion and inconsistency of camera position make the recognition of hand gestures difficult in any vision-based method. Diversity in finger shape, size, distribution and motion dynamics is also a big constraint. This leads to the motivation in developing a dense Scale Invariant Feature Transform (SIFT) flow based architecture for recognizing dynamic hand gestures. Initially, a combination of three frames differencing and skin filtering technique is used for hand detection to reduce the computational complexity followed by a SIFT flow technique to extract the features from the detected hand region. SIFT flow vectors obtained from every pixel can lead to overfilling, data redundancy and dimension disaster. A dual layer belief propagation algorithm is utilized to optimize the feature vectors to resolve the dimensionality problem. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers are used to evaluate the performance of the developed framework. Experiments were conducted on hand gesture database for HCI, Sebastien Marcel Dynamic Hand Posture Database and RWTH German finger spelling database. The simulation results demonstrate that the developed architecture has excellent performance on the uneven background and varying camera position and it is robust against image noise. A comparative analysis with the state of the art methods illustrates the effectiveness of the architecture.

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