Real Time Hand Gesture Recognition for Human Computer Interaction

Agrawal, Rishabh; Gupta, Nikita

doi:10.1109/iacc.2016.93

Cited by 40 publications

(15 citation statements)

References 15 publications

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“…Other important works in this field includes recognition in real-time [5][6][7][8][9]. Each of these works use several techniques such as image processing techniques, Principal component analysis and image segmentation.…”

Section: Related Workmentioning

confidence: 99%

Hand Gesture Recognition for Emoji and Text Prediction

2019

IJITEE

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Emoticons' are ideograms and smileys utilized in electronic messages and website pages. Emoticons exist in different classifications, including outward appearances, regular items, places and kinds of climate, and creatures. They are much similar to emojis, however emoticons are real pictures rather than typo graphics. This undertaking perceives the emoticons utilizing hand motions. We are detecting hand gestures and preparing a Convolutional Neural Network (CNN) model on a training dataset. We will make a database of hand gestures and train them. The system utilized here is a CNN. We are utilizing the SIFT filter to identify the hand and CNN for preparing the model. SIFT filter give a lot of highlights of an image that are not influenced by numerous factors, for example, object scaling and rotation. The SIFT filtering procedure comprises of two areas. The first is a procedure to identify intrigue focuses in the hand. Intrigue focuses are the points in the image in a 2D space that surpasses some limit measure and is better than straight forward edge recognition. The second segment is a procedure to make a vector like descriptor and this is the most special and prevalent part of the SIFT filter.

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Section: Related Workmentioning

confidence: 99%

Hand Gesture Recognition for Emoji and Text Prediction

2019

IJITEE

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“…They employed the Support Vector Machine (SVM) and K-Nearest Neighborhood (KNN) classifiers and obtained a 97.63% accuracy for the JAFFE database and 94.8% accuracy for the Cohn-Kanade database. Lastly, Rishabh Agrawal et al propose in [31] a method to recognize hand movements and gestures in order to interact with computers. It uses a Senz3D camera and analyzes the 3D data to classify hand gestures.…”

Section: Related Workmentioning

confidence: 99%

System for monitoring the wellness state of people in domestic environments employing emoticon-based HCI

et al. 2017

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Wellness state is affected by the habitability state of the domestic environment. Monitoring it can help to discover the causes of a low wellness levels aiding people in the improvement of their quality of life. In this paper, we propose a system to monitor the wellness state of people utilizing Liketrt's scale to determine the state of the user through an emoticon-based Human-Computer Interaction (HCI). The system is intended for domestic environments and measures the habitability conditions of the dwelling (such as temperature, humidity, luminosity and noise) employing sensors. An algorithm is designed in order to establish how to measure those conditions and to calculate the statistics that allows tracking their progress. The obtained information is presented to the user to compare his/her wellness state with the habitability conditions. Measures in a real domestic environment were performed in order to determine the configuration of our system. The energy-efficiency of the algorithm provides an improvement between 99.36% and 99.62% in the energy consumption depending on the selected parameters.

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“…Rishabh Agrawal et al, 2016 [6]presented that the most of the human computer interaction interfaces that are designed today require explicit instructions from the user in the form of keyboard taps or mouse clicks. As the complexity of these devices increase, the sheer amount of such instructions can easily disrupt, distract and overwhelm users.…”

Section: Review Of Literaturementioning

confidence: 99%

Enhance Real Time Hand Gesture Recognition using FCM Clustering and Nueral Network Approach

Kaur¹

2018

IJRASET

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Gesture Recognition is one of the most popular and viable solution for improving Human Computer Interaction and it has become very popular in the recent years due to its use in gaming devices like Xbox, PS4 and other devices like laptops, smartphones etc. Gesture Recognition and more specifically hand gesture recognition has usage in various application like medicine, accessibility support etc. Hand gesture recognition system can be used for interfacing between computer and human using hand gesture. This work defines a method for a human computer interface through hand gesture recognition that is able to recognize 25 static gestures from the American Sign Language hand alphabet. In this research work, proposed there arises a great demand of overcoming the existing problems to achieve greater accuracy, faster response time and the difficulties faced in handling the training data inputs and testing outputs. Hence, gives the need to use such algorithms each of which is unique in its own effective and creative way and try to bring out the best possible results. Our proposed work focuses on segment the gesture using FCM algorithm, extracting the features using (SIFT) Scale Invariant Feature Transform algorithm and classifying the gestures using deep neural network. By doing this our system obtains better performance in terms of classification parameters (Accuracy, FAR, FRR and Recognize Speed) and faster response time or delayed outputs.

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Real Time Hand Gesture Recognition for Human Computer Interaction

Cited by 40 publications

References 15 publications

Hand Gesture Recognition for Emoji and Text Prediction

Hand Gesture Recognition for Emoji and Text Prediction

System for monitoring the wellness state of people in domestic environments employing emoticon-based HCI

Enhance Real Time Hand Gesture Recognition using FCM Clustering and Nueral Network Approach

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