Real-time hand posture recognition using hand geometric features and Fisher Vector

Fang, Linpu; Liang, Ningxin; Kang, Wenxiong; Wang, Zhiyong; Feng, David Dagan

doi:10.1016/j.image.2019.115729

Cited by 19 publications

(5 citation statements)

References 20 publications

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“…It can be seen that convolutional neural network has obvious advantages in basketball pose recognition, and the recognition effect is Table 1 lists the recognition results based on random forest [23] and SVM [24]). Especially for attitude recognition results based on improved Gaussian kernel function [25], pose1 and Pose5 are compared, the recognition effects of Gaussian kernel function are 98% and 96%, while the recognition rates of Pose1 and Pose5 described in this paper are about 98.16% and 97.42%, indicating that the convolutional network model has a better performance in attitude recognition. As the data set and posture selected are different from other recognition methods, the comparison can only be made on the average recognition rate.…”

Section: Eai Endorsed Transactions On Scalable Information Systemsmentioning

confidence: 74%

Basketball posture recognition based on HOG feature extraction and convolutional neural network

Gao¹

2018

ICST Transactions on Scalable Information Systems

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Basketball posture recognition is one of the important research topics in human-computer interaction and physical education, which is of great significance in medical treatment, sports, security and other aspects. With the development of machine learning, the application value of basketball pose recognition in physical education is becoming more and more extensive. This paper constructs a novel convolutional neural network model to recognize basketball posture. The model consists of 11 layers. Convolution and pooling operations are carried out for five basketball postures in the sampled data set. By fusing with the features extracted from HOG, finer features can be obtained. Finally, the data set is trained and recognized by entering the full connection layer for classification. The results show that compared with the traditional machine learning methods, the recognition performance of new model is better.

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Section: Eai Endorsed Transactions On Scalable Information Systemsmentioning

confidence: 74%

Basketball posture recognition based on HOG feature extraction and convolutional neural network

Gao¹

2018

ICST Transactions on Scalable Information Systems

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“…Non-Negative Matrix Factorization + Compressive Sensing [42] 87.8 AlexNet 'FC6' + PCA and SVM [43] 95 Set of Geometric Features + Fisher Vector and SVM [44] 95.3 DeReFNets [45] 96.14 Our method 97.44…”

Section: Methods Recognition Rate [%]mentioning

confidence: 99%

A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition

Warchoł,

Kapuściński

2023

Applied Sciences

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Automatic recognition of hand postures is an important research topic with many applications, e.g., communication support for deaf people. In this paper, we present a novel four-stage, Mahalanobis-distance-based method for hand posture recognition using skeletal data. The proposed method is based on a two-stage classification algorithm with two additional stages related to joint preprocessing (normalization) and a rule-based system, specific to hand shapes that the algorithm is meant to classify. The method achieves superior effectiveness on two benchmark datasets, the first of which was created by us for the purpose of this work, while the second is a well-known and publicly available dataset. The method’s recognition rate measured by leave-one-subject-out cross-validation tests is 94.69% on the first dataset and 97.44% on the second. Experiments, including comparison with other state-of-the-art methods and ablation studies related to classification accuracy and time, confirm the effectiveness of our approach.

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“…The above accuracy was obtained with the combined features of Gaubor filter (GB) and Zernike moments (ZM) with a holdout CV test. Furthermore, a set of geometric features (SoGF) [ 27 ] such as angle, distance and curvature features, defined as local descriptors, was obtained from the contour of the hand gesture. Afterward, local descriptors were optimized using the Fisher vector, and the gestures were recognized using the support vector machine (SVM) classifier.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network

Sahoo

Prakash

Pławiak

et al. 2022

Sensors

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Hand gesture recognition is one of the most effective modes of interaction between humans and computers due to being highly flexible and user-friendly. A real-time hand gesture recognition system should aim to develop a user-independent interface with high recognition performance. Nowadays, convolutional neural networks (CNNs) show high recognition rates in image classification problems. Due to the unavailability of large labeled image samples in static hand gesture images, it is a challenging task to train deep CNN networks such as AlexNet, VGG-16 and ResNet from scratch. Therefore, inspired by CNN performance, an end-to-end fine-tuning method of a pre-trained CNN model with score-level fusion technique is proposed here to recognize hand gestures in a dataset with a low number of gesture images. The effectiveness of the proposed technique is evaluated using leave-one-subject-out cross-validation (LOO CV) and regular CV tests on two benchmark datasets. A real-time American sign language (ASL) recognition system is developed and tested using the proposed technique.

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Real-time hand posture recognition using hand geometric features and Fisher Vector

Cited by 19 publications

References 20 publications

Basketball posture recognition based on HOG feature extraction and convolutional neural network

Basketball posture recognition based on HOG feature extraction and convolutional neural network

A Four-Stage Mahalanobis-Distance-Based Method for Hand Posture Recognition

Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network

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