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  1. 1. A REAL TIME 3D STATIC HAND GESTURE RECOGNITION SYSTEM USING HCI FOR RECOGNITION OF NUMBERS First Author#, Second Author*, Third Author# # First-Third Department, First-Third University Address 1first.author@first-third.edu 3third.author@first-third.edu * Second Company Address Including Country Name 2 second.author@second.com Abstract— In this paper, we introduce a static hand gestures belongs to sign language. The proposed static gesture recognition system to recognize numbers from 0 hand gesture recognition system makes use of Human to 9. This system uses a single camera without any Computer Interaction (HCI) and computer vision. marker or glove on the hand. This work proposes an Human–computer Interaction (HCI) involves the easy-to-use and inexpensive approach to recognize single study, planning, and design of the interaction between handed static gestures accurately. The system helps people (users) and computers. A basic goal of HCI is millions of deaf people to communicate with other to improve the interactions between users and normal people. It describes a hand gesture recognition system (HGRS) which recognizes hand gestures in a computers by making computers more usable and vision based setup that includes capturing an image receptive to the users needs. using a webcam. The template matching algorithm is Computer vision is a field that includes methods for used for hand gesture recognition. It is mainly divided acquiring, processing, analyzing, and understanding into the following stages: image capturing, image pre- images and, in general, high-dimensional data from processing, region extraction, feature extraction and the real world in order to produce numerical or matching and gesture recognition. The image is first symbolic information. Computer vision is also captured in RGB format. The image pre-processing described as the enterprise of automating and module transforms the raw image into the desirable integrating a wide range of processes and feature vector which mainly includes converting the colour images into the HSV images and reducing noise. representations for vision perception. The region extraction module extracts the skin region Nowadays, the majority of the human-computer from the whole image and eliminates the forearm region interaction (HCI) is based on devices such as giving the region of interest. The feature extraction keyboard and mouse. Physically challenged people module extracts a set of distinct parameters to represent may have difficulties with such input devices and may each gesture and distinguish the different gestures. require a new means of entering commands or data Finally the features are matched and the corresponding into the computer. Gesture, Speech and touch inputs gesture is recognized. 100 images for each hand gesture are few possible means of addressing such users representing different numbers are used to train the system and then it is tested for a different set of images. needs to solve this problem. Using computer vision, a Images for the training set are taken, keeping the hand computer can recognize and perform the users gesture at a distance of 15 inches from a 10 megapixel camera. command, thus alleviating the need for a keyboard. Sign language is the most natural and expressive way for the hearing impaired. The Indian Sign Language was proposed by Government of India so Keywords— Region Extraction, Feature extraction, Gesture recognition, Single handed gestures. that there is a uniform sign language that can be used by all the deaf and dumb people in the country. I. INTRODUCTION Automatic sign language recognition offers Hand gesture recognition has various applications enhancement of communication capabilities for the like computer games, machinery control and thorough speech and hearing impaired. It promises improved mouse replacement. One of the most structured sets of social opportunities and integration in the society to these people [1]. J.Pansare [2] proposed a method in which the minimum gesture from the training dataset. This method gave a highEuclidian distance would determine the perfect matching accuracy at a comparatively low cost. Panwar [3] presented a
  2. 2. real time system for hand gesture recognition on the basis of In [13] a hand gesture recognition system to translate handdetection of some meaningful shape based features like gestures into Urdu alphabets using colour segmentation and aorientation, centre of mass, status of fingers, thumb in terms comprehensive classification scheme.of raised or folded fingers of hand and their respective Liu, Gan and Sun [14] proposed an algorithm based on Hulocation in the image. This approach is simple, easy to moments and Support Vector Machines (SVM). Firstly, Huimplement and does not require significant amount of training invariant moments are used to obtain feature vectors and thenor post processing, providing high recognition rate with a SVM is used to find a decision border between theminimum computation time. integrated hand and the defected hand. It brings a 3.5% error Dardas and Georganas proposed a system which included rate of identifying the hand.detecting and tracking bare hands in a cluttered background A simple recognition algorithm that uses three shape-basedusing skin detection and hand posture contour comparison features of a hand to identify what gesture it is conveying isafter face subtraction, recognizing hand gestures via bag of proposed in [15]. This algorithm takes an input image of afeatures and multiclass support vector machines and building hand gesture and calculates three features of the image, twoa grammar that generates gesture commands to control an based on compactness and one based on radial distance.application [4]. F.Ullah [5] presented a hand gesture recognition system II. PROPOSED SYSTEMthat uses an evolutionary programming technique called 1) Image CapturingCartesian Genetic Programming (CGP) which is faster incontrast to conventional Genetic Programming. The captured image is in RGB format. RGB images do not A hand gesture based human computer interaction system use a palette. The colour of each pixel is determined by theis proposed in [6] which uses a robust method to detect and combination of the red, green, and blue intensities stored inrecognize single stroke gestures traced with fingertips which each colour plane at the pixels location.are tracked in air by the camera using ‘Camshift’ tracker and Normalization of Imageare then translated into actions. Normalization is a process that changes the range Fernando [7] presented a less costly approach to develop a of pixel intensity values for images with poor contrast due tocomputer vision based sign language recognition application glare.in real time context with motion recognition. Normalization transforms an n- In [8] a vision based human computer interface system was dimensional grayscale imageproposed that can interpret a user’s gestures in real time tomanipulate games and windows using a 3D depth camera,which is more robust than the method using a general camera. with intensity values in the range (Min,Max), into a new A new sub-gesture modelling approach was proposed in [9] imagewhich represents each gesture as a sequence of fixed sub-gestures and performs gesture spotting where the gestureboundaries are identified. It outperforms state-of-the-artHidden Conditional Random Fields (HCRF) based methodsand baseline gesture potting techniques. with intensity values in the range (newMin,newMax). In [10] a hand gesture recognition system using a stereo Conversion from normalized RGB to YCbCrcamera was implemented in real time. It performed hand YCbCr is a family of colour spaces in which Y isdetection using a depth map, detected region of interest(ROI) the luma component and CB and CR are the blue-differenceusing a convex hull, calculate the depth of the object in ROI to and red-difference chroma components.obtain hand images that are more accurate and uses a blob YCbCr is not an absolute colour space; rather, it is a way oflabelling method to obtain a clean hand image. Finally uses encoding RGB information. It is used since unlike RGB it isZhang and Suen’s thinning algorithm to obtain the feature insensitive to luminescence and hence can detect all shades ofpoints to recognize the gestures. skin. Zhang and Yun [11] uses skin colour segmentation anddistance distribution feature to realize the gesture recognition 2) Gray thresholdingand added the colour marker to get rid of the independentregions. This method has good robustness and it can detect The graythresh function uses Otsus method, which choosesand recognize the hand gestures in varying illumination the threshold to minimize the intraclass variance of the blackconditions, hand distance and hand angles efficiently. and white pixels by reduction of a graylevel image to a binary Choras [12] proposed a method for the recognition of hand image.gestures using geometrical and Radon Transform (RT)features. The hand gesture recognition is realized based on the 3) Noise Removalgesture blob and texture parameters extracted with the blocks,RT image and also invariant moments giving a detection rate Median filtering is a nonlinear operation often used inof 94%. image processing to reduce "salt and pepper" noise.
  3. 3. Extraction of Region of Interest All title and author details must be in single-column format In order to extract the hand from the image, we use the and must be centered.concept of largest blob detection. Blob detection refers to Every word in a title must be capitalized except for shortvisual modules that are aimed at detecting points and/or minor words such as “a”, “an”, “and”, “as”, “at”, “by”, “for”,regions in the image that differ in properties like brightness or “from”, “if”, “in”, “into”, “on”, “or”, “of”, “the”, “to”, “with”.color compared to the surrounding. Author details must not show any professional title (e.g. Managing Director), any academic title (e.g. Dr.) or any 4) Edge Detection membership of any professional organization (e.g. Senior Member IEEE). Edge detection aims at identifying points in a digital To avoid confusion, the family name must be written as theimage at which the image brightness changes sharply or, more last part of each author name (e.g. John A.K. Smith).formally, has discontinuities Each affiliation must include, at the very least, the name of the company and the name of the country where the author is 5) Histogram Calculation based (e.g. Causal Productions Pty Ltd, Australia). Email address is compulsory for the corresponding author. We calculate a histogram for the image above a grayscalecolour bar. The number of bins in the histogram is specified A. Section Headingsby the image type. If it is a grayscale image, it uses a default No more than 3 levels of headings should be used. Allvalue of 256 bins. If it is a binary image, it uses two bins. In headings must be in 10pt font. Every word in a heading mustour system, we use a binary image. be capitalized except for short minor words as listed in Section III-B. 6) Pattern Recognition using Euclidean distance 1) Level-1 Heading: A level-1 heading must be in Small Caps, centered and numbered using uppercase Roman The histogram of the test image is compared with the numerals. For example, see heading “III. Page Style” of thishistogram of the images in the training set using Euclidean document. The two level-1 headings which must not bedistance to recognize the gesture. numbered are “Acknowledgment” and “References”. Euclidean distance = sqrt((x2-y2)2+(x1-y1)2) 2) Level-2 Heading: A level-2 heading must be in Italic, III.Page Style left-justified and numbered using an uppercase alphabetic All paragraphs must be indented. All paragraphs must be letter followed by a period. For example, see heading “C.justified, i.e. both left-justified and right-justified. Section Headings” above. Text Font of Entire Document 3) Level-3 Heading: A level-3 heading must be indented, The entire document should be in Times New Roman or in Italic and numbered with an Arabic numeral followed by aTimes font. Type 3 fonts must not be used. Other font types right parenthesis. The level-3 heading must end with a colon.may be used if needed for special purposes. The body of the level-3 section immediately follows the level- Recommended font sizes are shown in Table 1. 3 heading in the same paragraph. For example, this paragraph Title and Author Details begins with a level-3 heading. Title must be in 24 pt Regular font. Author name must bein 11 pt Regular font. Author affiliation must be in 10 pt B. Figures and TablesItalic. Email address must be in 9 pt Courier Regular font. Figures and tables must be centered in the column. Large figures and tables may span across both columns. Any table TABLE I or figure that takes up more than 1 column width must be FONT SIZES FOR PAPERS positioned either at the top or at the bottom of the page. Font Appearance (in Time New Roman or Times) Graphics may be full color. All colors will be retained on Size Regular Bold Italic the CDROM. Graphics must not use stipple fill patterns 8 table caption (in reference item because they may not be reproduced properly. Please use Small Caps), (partial) only SOLID FILL colors which contrast well both on screen figure caption, and on a black-and-white hardcopy, as shown in Fig. 1. reference item 9 author email address abstract abstract heading (in Courier), body (also in Bold) cell in a table 10 level-1 heading (in level-2 heading, Small Caps), level-3 heading, paragraph author affiliation 11 author name 24 Title
  4. 4. D. Table Captions Tables must be numbered using uppercase Roman numerals. Table captions must be centred and in 8 pt Regular font with Small Caps. Every word in a table caption must be capitalized except for short minor words as listed in Section III-B. Captions with table numbers must be placed before their associated tables, as shown in Table 1. E. Page Numbers, Headers and Footers Page numbers, headers and footers must not be used. F. Links and Bookmarks All hypertext links and section bookmarks will be removedFig. 1 A sample line graph using colors which contrast well both on screenand on a black-and-white hardcopy from papers during the processing of papers for publication. If you need to refer to an Internet email address or URL in your paper, you must type out the address or URL fully in Fig. 2 shows an example of a low-resolution image which Regular font.would not be acceptable, whereas Fig. 3 shows an example ofan image with adequate resolution. Check that the resolutionis adequate to reveal the important detail in the figure. Please check all figures in your paper both on screen and ona black-and-white hardcopy. When you check your paper ona black-and-white hardcopy, please ensure that: • the colors used in each figure contrast well, • the image used in each figure is clear, • all text labels in each figure are legible.C. Figure Captions Figures must be numbered using Arabic numerals. Figurecaptions must be in 8 pt Regular font. Captions of a singleline (e.g. Fig. 2) must be centered whereas multi-line captionsmust be justified (e.g. Fig. 1). Captions with figure numbersmust be placed after their associated figures, as shown inFig. 1. Fig. 2 Example of an unacceptable low-resolution image Fig. 3 Example of an image with acceptable resolution
  5. 5. G. References Causal Productions has used its best efforts to ensure that the The heading of the References section must not be templates have the same appearance.numbered. All reference items must be in 8 pt font. Please ACKNOWLEDGMENTuse Regular and Italic styles to distinguish different fields asshown in the References section. Number the reference items The heading of the Acknowledgment section and theconsecutively in square brackets (e.g. [1]). References section must not be numbered. When referring to a reference item, please simply use the Causal Productions wishes to acknowledge Michael Shellreference number, as in [2]. Do not use “Ref. [3]” or and other contributors for developing and maintaining the“Reference [3]” except at the beginning of a sentence, e.g. IEEE LaTeX style files which have been used in the“Reference [3] shows …”. Multiple references are each preparation of this template. To see the list of contributors,numbered with separate brackets (e.g. [2], [3], [4]–[6]). please refer to the top of file IEEETran.cls in the IEEE LaTeX Examples of reference items of different categories shown distribution.in the References section include: REFERENCES • example of a book in [1] [1] S. M. Metev and V. P. Veiko, Laser Assisted Microtechnology, 2nd • example of a book in a series in [2] ed., R. M. Osgood, Jr., Ed. Berlin, Germany: Springer-Verlag, 1998. • example of a journal article in [3] [2] J. Breckling, Ed., The Analysis of Directional Time Series: Applications to Wind Speed and Direction, ser. Lecture Notes in • example of a conference paper in [4] Statistics. Berlin, Germany: Springer, 1989, vol. 61. • example of a patent in [5] [3] S. Zhang, C. Zhu, J. K. O. Sin, and P. K. T. Mok, “A novel ultrathin • example of a website in [6] elevated channel low-temperature poly-Si TFT,” IEEE Electron Device Lett., vol. 20, pp. 569–571, Nov. 1999. • example of a web page in [7] [4] M. Wegmuller, J. P. von der Weid, P. Oberson, and N. Gisin, “High • example of a databook as a manual in [8] resolution fiber distributed measurements with coherent OFDR,” in Proc. ECOC’00, 2000, paper 11.3.4, p. 109. • example of a datasheet in [9] [5] R. E. Sorace, V. S. Reinhardt, and S. A. Vaughn, “High-speed digital- • example of a master’s thesis in [10] to-RF converter,” U.S. Patent 5 668 842, Sept. 16, 1997. [6] (2002) The IEEE website. [Online]. Available: http://www.ieee.org/ • example of a technical report in [11] [7] M. Shell. (2002) IEEEtran homepage on CTAN. [Online]. Available: • example of a standard in [12] http://www.ctan.org/tex- archive/macros/latex/contrib/supported/IEEEtran/ III. CONCLUSIONS [8] FLEXChip Signal Processor (MC68175/D), Motorola, 1996. [9] “PDCA12-70 data sheet,” Opto Speed SA, Mezzovico, Switzerland. The version of this template is V2. Most of the formatting [10] A. Karnik, “Performance of TCP congestion control with rateinstructions in this document have been compiled by Causal feedback: TCP/ABR and rate adaptive TCP/IP,” M. Eng. thesis, IndianProductions from the IEEE LaTeX style files. Causal Institute of Science, Bangalore, India, Jan. 1999. [11] J. Padhye, V. Firoiu, and D. Towsley, “A stochastic model of TCPProductions offers both A4 templates and US Letter templates Reno congestion avoidance and control,” Univ. of Massachusetts,for LaTeX and Microsoft Word. The LaTeX templates Amherst, MA, CMPSCI Tech. Rep. 99-02, 1999.depend on the official IEEEtran.cls and IEEEtran.bst files, [12] Wireless LAN Medium Access Control (MAC) and Physical Layerwhereas the Microsoft Word templates are self-contained. (PHY) Specification, IEEE Std. 802.11, 1997.
  6. 6. G. References Causal Productions has used its best efforts to ensure that the The heading of the References section must not be templates have the same appearance.numbered. All reference items must be in 8 pt font. Please ACKNOWLEDGMENTuse Regular and Italic styles to distinguish different fields asshown in the References section. Number the reference items The heading of the Acknowledgment section and theconsecutively in square brackets (e.g. [1]). References section must not be numbered. When referring to a reference item, please simply use the Causal Productions wishes to acknowledge Michael Shellreference number, as in [2]. Do not use “Ref. [3]” or and other contributors for developing and maintaining the“Reference [3]” except at the beginning of a sentence, e.g. IEEE LaTeX style files which have been used in the“Reference [3] shows …”. Multiple references are each preparation of this template. To see the list of contributors,numbered with separate brackets (e.g. [2], [3], [4]–[6]). please refer to the top of file IEEETran.cls in the IEEE LaTeX Examples of reference items of different categories shown distribution.in the References section include: REFERENCES • example of a book in [1] [1] S. M. Metev and V. P. Veiko, Laser Assisted Microtechnology, 2nd • example of a book in a series in [2] ed., R. M. Osgood, Jr., Ed. Berlin, Germany: Springer-Verlag, 1998. • example of a journal article in [3] [2] J. Breckling, Ed., The Analysis of Directional Time Series: Applications to Wind Speed and Direction, ser. Lecture Notes in • example of a conference paper in [4] Statistics. Berlin, Germany: Springer, 1989, vol. 61. • example of a patent in [5] [3] S. Zhang, C. Zhu, J. K. O. Sin, and P. K. T. Mok, “A novel ultrathin • example of a website in [6] elevated channel low-temperature poly-Si TFT,” IEEE Electron Device Lett., vol. 20, pp. 569–571, Nov. 1999. • example of a web page in [7] [4] M. Wegmuller, J. P. von der Weid, P. Oberson, and N. Gisin, “High • example of a databook as a manual in [8] resolution fiber distributed measurements with coherent OFDR,” in Proc. ECOC’00, 2000, paper 11.3.4, p. 109. • example of a datasheet in [9] [5] R. E. Sorace, V. S. Reinhardt, and S. A. Vaughn, “High-speed digital- • example of a master’s thesis in [10] to-RF converter,” U.S. Patent 5 668 842, Sept. 16, 1997. [6] (2002) The IEEE website. [Online]. Available: http://www.ieee.org/ • example of a technical report in [11] [7] M. Shell. (2002) IEEEtran homepage on CTAN. [Online]. Available: • example of a standard in [12] http://www.ctan.org/tex- archive/macros/latex/contrib/supported/IEEEtran/ III. CONCLUSIONS [8] FLEXChip Signal Processor (MC68175/D), Motorola, 1996. [9] “PDCA12-70 data sheet,” Opto Speed SA, Mezzovico, Switzerland. The version of this template is V2. Most of the formatting [10] A. Karnik, “Performance of TCP congestion control with rateinstructions in this document have been compiled by Causal feedback: TCP/ABR and rate adaptive TCP/IP,” M. Eng. thesis, IndianProductions from the IEEE LaTeX style files. Causal Institute of Science, Bangalore, India, Jan. 1999. [11] J. Padhye, V. Firoiu, and D. Towsley, “A stochastic model of TCPProductions offers both A4 templates and US Letter templates Reno congestion avoidance and control,” Univ. of Massachusetts,for LaTeX and Microsoft Word. The LaTeX templates Amherst, MA, CMPSCI Tech. Rep. 99-02, 1999.depend on the official IEEEtran.cls and IEEEtran.bst files, [12] Wireless LAN Medium Access Control (MAC) and Physical Layerwhereas the Microsoft Word templates are self-contained. (PHY) Specification, IEEE Std. 802.11, 1997.

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