Temporal Pyramid Matching of Local Binary Subpatterns for Hand-Gesture Recognition

Maqueda, Ana I.; del-Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso

doi:10.1109/lsp.2016.2579664

Cited by 16 publications

(8 citation statements)

References 16 publications

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“…e closed virtual reality system does not directly interact with the real world, and any operation does not have a direct effect on the real world [19].…”

Section: Methodsmentioning

confidence: 99%

Construction of Virtual Piano Performance System Based on Visual Gesture Recognition

Tian

2022

Mobile Information Systems

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In order for computers to understand people’s gestures through cameras and react accordingly, through in-depth research, gesture recognition technology in natural human-computer interaction is proposed. Combining natural human-computer interaction technology with music performance, using computer vision-based gestures, music is played in a virtual environment. Experiments show that the virtual piano has 14 piano keys. For the realization of piano performance, it is each piano key; once the piano key is greater than the set value, just call m_Wave.Load() to make a sound. According to the CWave class in the object-oriented MFC class library under VC++, create an object m_Wave of class CWave. Then, according to the m_Wave.Load() function of the CWave class, the connection of the sound is realized. It successfully solves the troubles of music lovers, enriches people’s spiritual life, and has certain practicability and scalability.

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“…e closed virtual reality system does not directly interact with the real world, and any operation does not have a direct effect on the real world [19].…”

Section: Methodsmentioning

confidence: 99%

Construction of Virtual Piano Performance System Based on Visual Gesture Recognition

Tian

2022

Mobile Information Systems

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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%

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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“…All test procedures are conducted using Matlab 2014a on a i5 2.7GHz computer with 8.00GB RAM. To analyze the proposed algorithm, we performed the set of experiments with three databases such as hand gesture database for HCI [4,33], Sebastien Marcel Dynamic Hand Posture Database [34] and RMTH German finger spelling database [35]. The performance parameters of the system are analyzed on the basis of certain metrics such as sensitivity, specificity and accuracy [36,37].…”

Section: Resultsmentioning

confidence: 99%

“…But it is sensitive to dynamic backgrounds. 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.…”

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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Temporal Pyramid Matching of Local Binary Subpatterns for Hand-Gesture Recognition

Cited by 16 publications

References 16 publications

Construction of Virtual Piano Performance System Based on Visual Gesture Recognition

Construction of Virtual Piano Performance System Based on Visual Gesture Recognition

Hand Gesture Recognizing Model Using Optimized Capsule Neural Network

Dense SIFT–Flow based Architecture for Recognizing Hand Gestures

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