Robust Hand Gestures Recognition Using a Deep CNN and Thermal Images

Breland, Daniel Skomedal; Dayal, Aveen; Jha, Ajit; Yalavarthy, Phaneendra K.; Pandey, Om Jee; Cenkeramaddi, Linga Reddy

doi:10.1109/jsen.2021.3119977

Cited by 33 publications

(17 citation statements)

References 41 publications

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“…This architecture is less sensitive to several typical variations and occlusions reducing the quality of gait recognition. The training time of this network for data contained within the CASIA-B database (124 people; less than 800 patterns) exceeded 9 min while the recognition time was 0.01 s. It is also worth mentioning that deep CNN is successfully used to classify various-sourced images as exemplified in the work of [ 35 ]. Article [ 36 ] presents the application of the Vision Transformer with an attention mechanism for gait recognition—or the GaitViT method.…”

Section: Related Workmentioning

confidence: 99%

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Derlatka

Borowska

2023

Sensors

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Human gait recognition is one of the most interesting issues within the subject of behavioral biometrics. The most significant problems connected with the practical application of biometric systems include their accuracy as well as the speed at which they operate, understood both as the time needed to recognize a particular person as well as the time necessary to create and train a biometric system. The present study made use of an ensemble of heterogeneous base classifiers to address these issues. A Heterogeneous ensemble is a group of classification models trained using various algorithms and combined to output an effective recognition A group of parameters identified on the basis of ground reaction forces was accepted as input signals. The proposed solution was tested on a sample of 322 people (5980 gait cycles). Results concerning the accuracy of recognition (meaning the Correct Classification Rate quality at 99.65%), as well as operation time (meaning the time of model construction at <12.5 min and the time needed to recognize a person at <0.1 s), should be considered as very good and exceed in quality other methods so far described in the literature.

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Section: Related Workmentioning

confidence: 99%

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Derlatka

Borowska

2023

Sensors

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“…David González León et al [ 26 ] used a deep camera and a lightweight convolutional neural network (CNN) model for the recognition of input gesture videos with higher accuracy on the test set. Daniel Skomedal Breland et al [ 27 ] proposed a robust gesture recognition system based on high-resolution thermal imaging for the accurate classification of high-resolution gestures by using deep CNNs.…”

Section: Related Workmentioning

confidence: 99%

Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN

Zhang

Yang

et al. 2023

Sensors

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Traditional human-computer interaction technology relies heavily on input devices such as mice and keyboards, which limit the speed and naturalness of interaction and can no longer meet the more advanced interaction needs of users. With the development of computer vision (CV) technology, research on contactless gesture recognition has become a new research hotspot. However, current CV-based gesture recognition technology has the limitation of a limited number of gesture recognition and cannot achieve fast and accurate text input operations. To solve this problem, this paper proposes an over-the-air handwritten character recognition system based on the coordinate correction YOLOv5 algorithm and a lightweight convolutional neural network (LGR-CNN), referred to as Air-GR. Unlike the direct recognition of captured gesture pictures, the system uses the trajectory points of gesture actions to generate images for gesture recognition. Firstly, by combining YOLOv5 with the gesture coordinate correction algorithm proposed in this paper, the system can effectively improve gesture detection accuracy. Secondly, considering that the captured gesture coordinates may contain multiple gestures, this paper proposes a time-window-based algorithm for segmenting the gesture coordinates. Finally, the system recognizes user gestures by plotting the segmented gesture coordinates in a two-dimensional coordinate system and feeding them into the constructed lightweight convolutional neural network, LGR-CNN. For the gesture trajectory image classification task, the accuracy of LGR-CNN is 13.2%, 12.2%, and 4.5% higher than that of the mainstream networks VGG16, ResNet, and GoogLeNet, respectively. The experimental results show that Air-GR can quickly and effectively recognize any combination of 26 English letters and numbers, and its recognition accuracy reaches 95.24%.

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“…Future research recommendations from the authors included testing other domains such as EEG and image classification using their proposed models.The low thermal image dataset, collected from multiple authors with 32 × 32 resolution, has been updated to correspond to 0–9 sign language digits using a high thermal image captured using a light-independent thermal camera that produces an array of 19,200 pixels with 160 × 20 resolution [ 33 ]. The employment of CNNs and thermal infrared images for hand 340 gesture recognition is tackled in [ 34 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Sign and Human Action Detection Using Deep Learning

2022

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Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model’s performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.

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Robust Hand Gestures Recognition Using a Deep CNN and Thermal Images

Cited by 33 publications

References 41 publications

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Air-GR: An Over-the-Air Handwritten Character Recognition System Based on Coordinate Correction YOLOv5 Algorithm and LGR-CNN

Sign and Human Action Detection Using Deep Learning

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