Tr 00104

IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017

Review on Hand Gesture Recognition Sindhu.K.M

Suresha.H.S

M.Tech student, Dept of E&C, Don Bosco Institute of Technology, Sindhu.matad@gmail.com

Associate Professor, Dept of E&C, Don Bosco Institute of Technology, srisuri75@gmail.com

Abstract: Hand gesture recognition method arriving great consideration in latest few years since of its manifoldness application and facility to interrelate by machine efficiently during human computer interaction. This paper mainly focuses on the survey on Hand Gesture Recognition. The hand gestures give a divide complementary modality to speech for express ones data. Hand gesture is the method of non-verbal communiquĂŠ for human beings for its freer expressions much more other than the body parts. Hand gesture detection has greater significance in scheme a competent human computer interaction method. This paper emphasis on different hand gesture approaches, technologies and applications.

Keywords: Hand Gesture Recognition, Segmentation, Feature Extraction and Classification

I.

INTRODUCTION

India is diversified in culture, language and religion. Since there is a great diversity among Indian languages, the literature survey reports the non-existence of standard forms of Indigenous Sign Language (LSL) gestures. ISL alphabets are derived from British Sign Language (BSL) and French Sign Language (FSL). Because of these problems, the standard database for the ISL / gesture alphabet has not been developed so far.

Few research works has been carried out on ISL recognition and interpretation through image processing / vision techniques. But these are only initial jobs proven with simple image processing techniques and are not treated with real-time data.

The classification technique refers to the Euclidean distance metric. Subsequently we propose a system to translate the input speech to ISL that is shown with the help of a 3D virtual human avatar. The input to the system is the speech of the employee who is in English.

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The speech recognition module recognizes speech and performs a text output. This text is then passed to a parser module that tokenizes the string and labels the part of the voice using a sample file.

The output of the analyzer is given to an eliminator module that performs a reduction task by removing unwanted elements and also the root form of the verbs are found using the stemmer module. The structural divergence of English and ISL is handled by a phrase reordering module using the ISL dictionary and the rule. This module generates ISL brightness strings that can be reproduced through virtual human 3D. A 3D animation module creates animation from motion-captured data. In this approach a lot of 3D model data is used which makes the system clumsy and bulk. Attempt to machine static translation as well as dynamic ISL gestures with image processing features such as skin tone detection space filter velocimetry and temporal tracking is developed. The representation of the power spectrum of each gesture is given as moving images. Edge detection, cropping and boundary tracking are used as characteristics for the recognition process. These methods work well for the static signs of ISL. They do not deal with the dynamic, global and local movements of ISL gestures. For example, the ISL signs of the letters A-B, M-N, U-V look similar. It is sometimes difficult for a human to correctly recognize the sign. When it comes to computers, the inter-class variability parameter must be considered II. LITERATURE SURVEY Giulio Marin et.al [01] introduces the two various gesture recognition methods for Leap Motion plus Kinect devices has been proposed.

Various feature sets are utilized to deal with dissimilar nature of information provided with two devices; Leap Motion gives a high level however more imperfect information description though Kinect gives the complete depth of map. Even if

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017 the information provided with Leap Motion is not absolutely dependable, since several fingers may not be identified, the planned set of the descriptions and classification method permits attained a highquality in accuracy. The more absolute description presented with depth map of Kinect permits capturing other properties omitted in Leap Motion yield by combine the two strategy a very highquality accuracy is attained. The experimental results demonstrate that the task of each finger to precise angular region lead to the substantial enlarge of the recital.

J. Rekbai et.al [02] proposes an advance to deal with inter-class uncertainty subject in ISL alphabet detection. Through the assist of localglobal fmger group data and shape-texture descriptions, accurate detection of every ISL symbols have been attained for mutually static & dynamic gesture. Conversely, suitable to less steady life of PCBR descriptions, the correctness slips downwards faintly in case of the dynamic signals of ISL. The future potential work concentrate on study of the dynamic nature of the gestures below dissimilar circumstances.

Chao Xu et.al [04] explore that how smart watch is utilized for gesture identification with finger-writing. They demonstrated that the smart watch sensors can correctly notice arm, dispense and even finger gesture. It can also display that watch identify the characters when addict writes on the surface by her guide finger. Gesture identification and finger- writing by smart watch is utilized to produce new application for an interacting by near devices and distantly controlling it. Then, they are designing effective touch-screen with methods to identify user's finger-writing in air on the smartwatch sensor.

Pavlo Molchanov et.al [05] developed an effectual process for energetic hand gesture identification by 3D convolutional neural network.

This classifier utilizes the combined motion amount of normalize the depth and picture gradient ideals; with utilize spatio-temporal information augmentation to evade over fitting. With means of the extensive assessment, they established that arrangement of the low and high declaration subnetworks advances categorization accuracy significantly. Further they established that the proposed information augmentation method acts a significant position in attaining superior presentation.

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Shalini Gupta et.al [06] introduces the new multi-sensor systems that recognize dynamic gesture of the drivers in the car. Preliminary experiments show that the dual employ of the color, short-range radar and depth sensors get better accuracy, robustness, with power utilization of gesture detection scheme. In future, they will discover by using micro-Doppler signature calculated by radar as the descriptions for gesture identification. They also increase the revise to larger information set of gesture more subject to advance the simplification of DNN; also expand the methodologies for constant online frame-wise motion identification.

Yang Zhang et.al [07] presented a wearable, low-cost and low power Electrical Impedance Tomography scheme for hand gesture identification. It process cross-sectional bio impedance by 8 electrodes on wearer’s skin. By 28 all-pairs capacity, software will improve interior impedance allocation, which can feed to hand gesture classification. They assess two gesture of sets (hand and pinch sets) with two body placement (wrist and arm). User learns marks illustrate that the advance can propose elevated correctness hand gesture identification when the scheme is skilled on wearer. Though, like mainly other bio-sensing system, marks corrupt when scheme is re-worn at presently time, or wear by other user.

ChenyangZhang et.al [08] proposes the ovel discriminative 3D descrip-tor (H3DF) method can effectively capture and replica rich surface shape data of depth maps. Apply the orientation normalization, forceful coding with concentric spatial pooling, the H3DF descriptor is robust to conversion, sight angle with scaling changes. Local H3DF can also able to develop into intense H3DF for form more local patterns. To tack lethet enquire of energetic hand gesture and human action identification as of the depth video sequence, the two temporal addition methods are urbanized: dynamic programming-based temporal partition and N-gram-based method. The two methods are applied to construct increased descriptors by robust representative explanation. They have extensively assessed the efficiency of anticipated H3DF descriptor on 4 public datasets counting static hand gesture identification from single depth picture, dynamic hand gesture and human act identification from depth sequence. Then experimental results show that proposed method outperform or achieve

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017 similar accuracy to state-of-the-art for act and hand gesture identification.

collected with a magnetic place tracker emotionally involved to user hand, however the method also appropriate to motion information gathered by vision based methods, inertial motion capture techniques or depth sensor. The motion information in absolute place format changed to representation through the motion capture stage.

Nurettin Cag˘rı Kılıboz [10] presents the easy yet powerful algorithm to identify and be familiar with trajectory-based dynamic hand gesture in actual time. The gestures can be representing with the ordered series of directional actions in the 2D space. Gesture information can

III. HAND GESTURE RECOGNITION

Feature Extraction

Segmentation

Classification

Input Hand Gesture Image

Result Performance

Fig.1: Proposed block diagram of hand gesture recognition

A. Hand Segmentation

B. Feature Extraction

Segmentation procedure is the first progression for the recognizing hand gestures. This is the method of separating the input picture (hand gesture image) into areas divided by limitations.

The segmentation method depends on sort of gesture, if it can be dynamic gesture then hand gesture require to be situated and track, it can static gesture (posture) input image is segmented only.

The hand must located initially, usually the bounding box utilized to identify the depending on skin color and next, the hand include to be track, for track the hand there is two main methods; either video seperated into frames and every frame contain to be process alone, in that case the hand frame can treated as the posture and segmented, or by several tracking data like shape, skin color by several filter. Fig.1 represents the general system for hand gesture recognition.

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The segmentation procedure leads to ideal features extraction method and latter act and significant role in doing well recognition procedure. The features vector of segmented image can extracted in various ways according to the particular appliance. Different methods are applied for representative the features can extracted.

Various methods utilized shape of hand while others employed fingertips place, palm center, etc. created 13 parameters as a feature vector, the primary parameters represent the ratio feature of bounding box of hand and rest 12 parameters are mean ideals of the brightness pixels in image.

C. Gesture Classification

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017

Fig.2 Gesture Representation

There are various algorithms to notice hand from an input image. Hand gesture identification methods were updated by technology changes. Based on this updates hand gesture recognition approaches can be classified into various categories is shows in above Fig.2. After modelling and study of an input hand image, gesture classification approaches are utilized to identify the gesture. The recognition procedure affected with proper assortment of the features parameter and appropriate classification method.

For instance the edge detection or contour operators cannot be utilized for gesture recognition since lots of hand postures produced and can create misclassification. The hand gesture is obtained in discover the hand gesture as of image and

Up

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distinctive hand as of background from the unwanted objects. Skin color provides an effectual and efficient for hand detection. Segmentation based skin color method applied for hand locate.

The recognition procedure can affected with the proper assortment of gesture parameters of descriptions and accuracy of its categorization.

IV. EXPECTED RESULTS In gesture recognition, uses series of images as the template. This form is especially easy as compared to previous residual two methods.

Among the help of these gestures we can handle the actions of hand; up, down, left and right is shows in Fig.3.

Down

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IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017

Available at: www.dbpublications.org

International e-Journal For Technology And Research-2017

Right

Left Fig.3: Hand gesture recognition

V. CONCLUSION Hand gesture recognition is discovery its application for nonverbal message among human and computer, general fit person and physically challenged people, 3D gaming, virtual reality etc.

With enlarge in applications, the gesture recognition method stress lots of investigate in various directions.

REFERENCES [1] Giulio Marin, Fabio Dominio and Pietro Zanuttigh,“Hand Gesture Recognition With Leap Motion And Kinect Devices”, IEEE, 2014.

[2] J. Rekbai, J. Bhattacharya and s.

Majumder “Shape, Texture and Local Movement Hand Gesture Features for Indian Sign Language Recognition”, IEEE, 2011.

[3] Siddharth S. Rautaray and Anupam Agrawal,“Vision based hand gesture recognition for human computer interaction: a survey”, Spinger, 2015.

[4] Chao Xu, Parth H. Pathak and Prasant Mohapatra,“Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch”, International Workshop, 2015.

[5] Pavlo Molchanov, Shalini Gupta, Kihwan Kim, and Jan Kautz, “Hand Gesture Recognition with 3D Convolutional Neural Networks”, IEEE, 2015 [6] Molchanov P, Gupta S, Kim K, and Pulli K,“Multi-sensor system for driver's handgesture recognition”, Vol. 1, pp. 1-8, IEEE, 2015.

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[7] Zhang Y and Harrison C, “Tomo: Wearable, low-cost electrical impedance tomography for hand gesture recognition”, pp. 167-173, IEEE, 2015.

[8] Zhang C and Tian Y, “Histogram of 3d facets: A depth descriptor for human action and hand gesture recognition”, Elsevier, 2015.

[9] Pisharady P K, andSaerbeck M, “Recent methods and databases in vision-based hand gesture recognition: A review”, IEEE, 2015.

[10] Kılıboz N C and Gudukbay U, “A hand gesture recognition technique for human– computer interaction”, Elseveir, 2015.

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