K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion

Gupta, Bhumika; Shukla, Pushkar; Mittal, Ankush

doi:10.1109/iccci.2016.7479951

Cited by 65 publications

(16 citation statements)

References 5 publications

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“…The preprocessing operations include resizing, converting, filtering, enhancing, and segmenting images. For instance, some techniques such as skin color detection and filtering, RGB to HSV, converting to gray scale, and hand segmentation are generally introduced [9,10]. Moreover, preprocessing is also performed during the testing phase to obtain the region of interest (ROI).…”

Section: Preprocessingmentioning

confidence: 99%

A Survey on Artificial Intelligence in Chinese Sign Language Recognition

et al. 2020

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Chinese Sign Language (CSL) offers the main means of communication for the hearing impaired in China. Sign Language Recognition (SLR) can shorten the distance between the hearing-impaired and healthy people and help them integrate into the society. Therefore, SLR has become the focus of sign language application research. Over the years, the continuous development of new technologies provides a source and motivation for SLR. This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR). With a thorough review of superior methods from 2000 to 2019 in CSLR researches, various techniques and algorithms such as scale-invariant feature transform, histogram of oriented gradients, wavelet entropy, Hu moment invariant, Fourier descriptor, gray-level co-occurrence matrix, dynamic time warping, principal component analysis, autoencoder, hidden Markov model (HMM), support vector machine (SVM), random forest, skin color modeling method, k-NN, artificial neural network, convolutional neural network (CNN), and transfer learning are discussed in detail, which are based on several major stages, that is, data acquisition, preprocessing, feature extraction, and classification. CSLR was summarized from some aspect as follows: methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets. The advantages and limitations of different CSLR approaches were compared. It was found that data acquisition is mainly through Kinect and camera, and the feature extraction focuses on hand's shape and spatiotemporal factors, but ignoring facial expressions. HMM and SVM are used most in the classification. CNN is becoming more and more popular, and a deep neural network-based recognition approach will be the future trend. However, due to the complexity of the contemporary Chinese language, CSLR generally has a lower accuracy than other SLR. It is necessary to establish an appropriate dataset to conduct comparable experiments. The issue of decreasing accuracy as the dataset increases needs to resolve. Overall, our study is hoped to give a comprehensive presentation for those people who are interested in CSLR and SLR and to further contribute to the future research.

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Section: Preprocessingmentioning

confidence: 99%

A Survey on Artificial Intelligence in Chinese Sign Language Recognition

et al. 2020

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“…In India, major research is focused on regional versions or manual components of signs. [6][7][8][9][10][11] reported work on dynamic hand gesture recognition for their dataset having limited number of gestures for single and two-hands manual signs. For Indian sign language translation, [12] and [13] proposed algorithms to convert ISL sentences in English text but they used traditional rule-based approach for sign translation due to limited size of their datasets.…”

Section: Introductionmentioning

confidence: 99%

Towards Indian Sign Language Sentence Recognition using INSIGNVID: Indian Sign Language Video Dataset

Mistree¹,

Thakor²,

Bhatt³

2021

IJACSA

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Sign language, a language used by Deaf community, is a fully visual language with its own grammar. The Deaf people find it very difficult to express their feelings to the other people, since the other people lack the knowledge of the sign language used by the Deaf community. Due to the differences in vocabulary and grammar of the sign languages, complete adoption of methods used for other international sign languages is not possible for Indian Sign Language (ISL) recognition. It is difficult to handle continuous sign language sentence recognition and translation into text as no large video dataset for ISL sentences is available. INSIGNVID -the first Indian Sign Language video dataset has been proposed and with this dataset as input, a novel approach is presented that converts video of ISL sentence in appropriate English sentence using transfer learning. The proposed approach gives promising results on our dataset with MobilNetV2 as pretrained model.

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“…Hence, this study attains 95.0% accuracy for recognition of Handicraft-Gesture data set and 89.5% accuracy for data set of Leap Motion gesture. Gupta et al (2016) has presented a methodology for recognizing hand gestures continuously via a three axis accelerometer and gyroscope sensors incorporated with a smart device. The inconstant impact of a hand showing a gesture can be decreased by proposing an algorithm for gesture coding.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of SVM-based hand gesture detection and recognition system using distance transform on different data sets for autonomous vehicle moving applications

Neethu

Suguna

Rajan³

2021

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Purpose This paper aims to propose a novel methodology for classifying the gestures using support vector machine (SVM) classification method. Initially, the Red Green Blue color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point (centroid) of palm region is detected and the fingertips are detected using SVM classification algorithm based on the detected centroids of the detected palm region. Design/methodology/approach Gesture is a physical indication of the body to convey information. Though any bodily movement can be considered a gesture, generally it originates from the movement of hand or face or combination of both. Combined gestures are quiet complex and difficult for a machine to classify. This paper proposes a novel methodology for classifying the gestures using SVM classification method. Initially, the color hand gesture image is converted into YCbCr image in preprocessing stage and then palm with finger region is segmented by threshold process. Then, distance transformation method is applied on the palm with finger segmented image. Further, the center point of the palm region is detected and the fingertips are detected using SVM classification algorithm. The proposed hand gesture image classification system is applied and tested on “Jochen Triesch,” “Sebastien Marcel” and “11Khands” data set hand gesture images to evaluate the efficiency of the proposed system. The performance of the proposed system is analyzed with respect to sensitivity, specificity, accuracy and recognition rate. The simulation results of the proposed method on these different data sets are compared with the conventional methods. Findings This paper proposes a novel methodology for classifying the gestures using SVM classification method. Distance transform method is used to detect the center point of the segmented palm region. The proposed hand gesture detection methodology achieves 96.5% of sensitivity, 97.1% of specificity, 96.9% of accuracy and 99.3% of recognition rate on “Jochen Triesch” data set. The proposed hand gesture detection methodology achieves 94.6% of sensitivity, 95.4% of specificity, 95.3% of accuracy and 97.8% of recognition rate on “Sebastien Marcel” data set. The proposed hand gesture detection methodology achieves 97% of sensitivity, 98% of specificity, 98.1% of accuracy and 98.8% of recognition rate on “11Khands” data set. The proposed hand gesture detection methodology consumes 0.52 s as recognition time on “Jochen Triesch” data set images, 0.71 s as recognition time on “Sebastien Marcel” data set images and 0.22 s as recognition time on “11Khands” data set images. It is very clear that the proposed hand gesture detection methodology consumes less recognition rate on “11Khands” data set when compared with other data set images. Hence, this data set is very suitable for real-time hand gesture applications with multi background environments. Originality/value The modern world requires more numbers of automated systems for improving our daily routine activities in an efficient manner. This present day technology emerges touch screen methodology for operating or functioning many devices or machines with or without wire connections. This also makes impact on automated vehicles where the vehicles can be operated without any interfacing with the driver. This is possible through hand gesture recognition system. This hand gesture recognition system captures the real-time hand gestures, a physical movement of human hand, as a digital image and recognizes them with the pre stored set of hand gestures.

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K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion

Cited by 65 publications

References 5 publications

A Survey on Artificial Intelligence in Chinese Sign Language Recognition

A Survey on Artificial Intelligence in Chinese Sign Language Recognition

Towards Indian Sign Language Sentence Recognition using INSIGNVID: Indian Sign Language Video Dataset

Performance evaluation of SVM-based hand gesture detection and recognition system using distance transform on different data sets for autonomous vehicle moving applications

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