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Title
Abstract
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Introduction
Sample of Images from Our Database
Image Preprocessing Result
Feature Extraction Result
Local Contour Sequence Result
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Computer Science
Computer Vision

A Static Hand Gesture Recognition Based on Local Contour Sequence

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Abstract

Today world is running behind the computer industries and pattern recognition is one of the important and vast fields of computer intelligence. Gesture recognition is one of the applications of Pattern recognition and further hand gesture recognition; hand gesture recognition system can be used as an interface between human hand and computer. Our technique provides a human hand interface with computer which can recognize static gestures from American Sign Language. Since 24 gestures from American Sign Language (ASL) are static so, we was able to recognize them. Our objective is to develop a hand gesture recognition system which can recognize most of the static characters from ASL with a good accuracy which can only work offline and is mainly dependent on database.

Figures (8)
me ae eee Saw eee Pattem is a group of objects, order or concepts where the elements of the one group are similar to one another in specific way/aspects. Pattern can be described by certain quantities, qualities, properties, features and so on. Example: Humans, Radar Signals, insects, Animals, sonar signals, Fossil records, Clouds etc [1], the art or ability of a computer to recognize the patterns correctly can be termed as Pattern Recognition. Pattern Recognition is "the act of taking input of raw data and extract specific and unique features from them to recognize them. A gesture is a form of non-verbal communication in which visible bodily actions can be used for communication [2]. It can be categorize into 1.Static 2.Dynamic [3]. The process of recognizing and predicting a gesture is known as Gesture Recognition and Sign language recognition is one of its applications. Sign language can involve combining orientation and movements of the hands, arms or body, hand shapes, and facial expressions to express thoughts and words which can be used for communication mostly by Deaf and Dumb people. It can also provide a good interface between computer and user, so in this paper we are representing a hand gesture recognition system which can recognize most of the character from ASL with a good accuracy. Our approach for hand gesture recognition is mainly database oriented for offline system so; our first problem is to gather a good quality of data since our classifier will classify characters according to it only. Then afterwards the collected data should be processed properly in order to remove noise, errors and unwanted data which can further create problem in feature extraction process and reduce the efficiency of system. Cameras are used to capture the picture of hand gesture so; they should be good enough to take a clear picture. Now the problem arises that sometimes camera can’t take the clear picture because of the position, orientation of hand gesture and its distance from the camera since it is not parallel to camera,
me ae eee Saw eee Pattem is a group of objects, order or concepts where the elements of the one group are similar to one another in specific way/aspects. Pattern can be described by certain quantities, qualities, properties, features and so on. Example: Humans, Radar Signals, insects, Animals, sonar signals, Fossil records, Clouds etc [1], the art or ability of a computer to recognize the patterns correctly can be termed as Pattern Recognition. Pattern Recognition is "the act of taking input of raw data and extract specific and unique features from them to recognize them. A gesture is a form of non-verbal communication in which visible bodily actions can be used for communication [2]. It can be categorize into 1.Static 2.Dynamic [3]. The process of recognizing and predicting a gesture is known as Gesture Recognition and Sign language recognition is one of its applications. Sign language can involve combining orientation and movements of the hands, arms or body, hand shapes, and facial expressions to express thoughts and words which can be used for communication mostly by Deaf and Dumb people. It can also provide a good interface between computer and user, so in this paper we are representing a hand gesture recognition system which can recognize most of the character from ASL with a good accuracy. Our approach for hand gesture recognition is mainly database oriented for offline system so; our first problem is to gather a good quality of data since our classifier will classify characters according to it only. Then afterwards the collected data should be processed properly in order to remove noise, errors and unwanted data which can further create problem in feature extraction process and reduce the efficiency of system. Cameras are used to capture the picture of hand gesture so; they should be good enough to take a clear picture. Now the problem arises that sometimes camera can’t take the clear picture because of the position, orientation of hand gesture and its distance from the camera since it is not parallel to camera,
FE SS ae, SA ee ee We had created our own database for each character of ASL, which can includes 504 images i.e. 21 images for each (24) gestures. During creating a database images captured should have uniform dark colour background that can be black with < white colour rubber glove on hand as in contrast. We had done this in order to minimize noise and unwanted data so that we can easily do segmentation process. The user has to wear a black colour cloth around his arm till wrist from the shoulder sc that black colour cloth can easily match with the background. Covered arm and the background should be of similar colour. Letter j and z are discarded because we can describe them dynamically only and our approach is for static gestures only. eee ——_ | ioe es iit | First of all we have to do pre processing of data which is very important task in Hand Gesture Recognition system. Preprocessing should be done on images initially before we extract features from data of Hand Gestures. It is to be done to remove noise, unwanted errors and to make data efficient so, that it can be used for further image processing. There are two steps for pre processing of data 1.Image Segmentation 2.Morphological Filtering.
FE SS ae, SA ee ee We had created our own database for each character of ASL, which can includes 504 images i.e. 21 images for each (24) gestures. During creating a database images captured should have uniform dark colour background that can be black with < white colour rubber glove on hand as in contrast. We had done this in order to minimize noise and unwanted data so that we can easily do segmentation process. The user has to wear a black colour cloth around his arm till wrist from the shoulder sc that black colour cloth can easily match with the background. Covered arm and the background should be of similar colour. Letter j and z are discarded because we can describe them dynamically only and our approach is for static gestures only. eee ——_ | ioe es iit | First of all we have to do pre processing of data which is very important task in Hand Gesture Recognition system. Preprocessing should be done on images initially before we extract features from data of Hand Gestures. It is to be done to remove noise, unwanted errors and to make data efficient so, that it can be used for further image processing. There are two steps for pre processing of data 1.Image Segmentation 2.Morphological Filtering.
nesult or the gestures alter image rreprocessing Preprocessing result are shown above and all the experiments are performed in MATLAB. After the preprocessing we get a smooth and better hand gesture which can results a better efficiency. [) os a better smooth, closed and contour of a gesture. Dilation, Erosion, Opening, Closing are the basic operators that works in morphological filtering [8].
nesult or the gestures alter image rreprocessing Preprocessing result are shown above and all the experiments are performed in MATLAB. After the preprocessing we get a smooth and better hand gesture which can results a better efficiency. [) os a better smooth, closed and contour of a gesture. Dilation, Erosion, Opening, Closing are the basic operators that works in morphological filtering [8].
Wily WC fai sciected Canny Lage petector. 10S UOMIPal Ison WIL OULC! dVdliaDle UCIMMUUCS, Images can be optimized in all possible four directions i.e. vertical, diagonal and horizontal and determines the most sensitive available edge from the four directions. Edge detection is very difficult in noisy images as it is not possible to plot exact edges of the original image since noise also have high intensity and it can intrupt. This results less accurate edge detection, attempts are made to remove noise from images but that can blurred or distort edges. So, we are facing many problems like false edge detection, high computational time, missing true edges, edge localization and problems due to noice etc. Therefore, We have to select an appropriate and better edge detection technique for our database. The main aim of the edge detection technique is to detect the the true accurate edge without effecting the properties of the image [11]. Still, edge detection techniques are grouped into two categories i.e. gradient and Laplacian.
Wily WC fai sciected Canny Lage petector. 10S UOMIPal Ison WIL OULC! dVdliaDle UCIMMUUCS, Images can be optimized in all possible four directions i.e. vertical, diagonal and horizontal and determines the most sensitive available edge from the four directions. Edge detection is very difficult in noisy images as it is not possible to plot exact edges of the original image since noise also have high intensity and it can intrupt. This results less accurate edge detection, attempts are made to remove noise from images but that can blurred or distort edges. So, we are facing many problems like false edge detection, high computational time, missing true edges, edge localization and problems due to noice etc. Therefore, We have to select an appropriate and better edge detection technique for our database. The main aim of the edge detection technique is to detect the the true accurate edge without effecting the properties of the image [11]. Still, edge detection techniques are grouped into two categories i.e. gradient and Laplacian.
Local Contour Sequence Result: C lassification Result: NX 1G001LLIVGUVUIL, After edge detection we got the boundary of the ‘hand gestures from our database, now we applied Localized Contou Sequence (LCS) technique for our classification process. On applying the LCS on the database it tracks the contour ir clockwise direction and the contour pixels are numbered sequentially [9] [14]. First it search the top most non zero pixel anc from that pixel it starts a numbering in a clockwise direction in a sequential manner. The no of pixels in the contour can vary due to the amount of light, quality of camera used and the distance of a gesture from camera. Scaling of the amplitude of < LCS can be adjusted by dividing the output samples of the LCS by the standard deviation of the LCS [9]. Scaling can also he adjusted by just uniformly expanding or compressing the samples of LCS to obtain fixed contour by using UP or DOWN sampler [9] [15]. Up sampler is used to increase the sampling rate by using an integer factor and DOWN sampler is used tc decrease the sampling rate by using an integer factor, that integer factor can be any positive integer [15]. T acral Cantor Sagquanca PR aadilt: After getting the LCS for each gesture image we can classify them into different classes i.e. every character from American Sign Language belongs to different class. This can be done by putting a threshold line which will be unique for every
Local Contour Sequence Result: C lassification Result: NX 1G001LLIVGUVUIL, After edge detection we got the boundary of the ‘hand gestures from our database, now we applied Localized Contou Sequence (LCS) technique for our classification process. On applying the LCS on the database it tracks the contour ir clockwise direction and the contour pixels are numbered sequentially [9] [14]. First it search the top most non zero pixel anc from that pixel it starts a numbering in a clockwise direction in a sequential manner. The no of pixels in the contour can vary due to the amount of light, quality of camera used and the distance of a gesture from camera. Scaling of the amplitude of < LCS can be adjusted by dividing the output samples of the LCS by the standard deviation of the LCS [9]. Scaling can also he adjusted by just uniformly expanding or compressing the samples of LCS to obtain fixed contour by using UP or DOWN sampler [9] [15]. Up sampler is used to increase the sampling rate by using an integer factor and DOWN sampler is used tc decrease the sampling rate by using an integer factor, that integer factor can be any positive integer [15]. T acral Cantor Sagquanca PR aadilt: After getting the LCS for each gesture image we can classify them into different classes i.e. every character from American Sign Language belongs to different class. This can be done by putting a threshold line which will be unique for every
iter if —_ FF Preece contour. That is every class of contour will have its unique threshold value and according to that threshold value we can able generate a unique code corresponding to that gesture class and that will be also unique for that class. That code can be generated by identifying the significant peaks and insignificant peaks from the contour and encode them from 0 and 1 respectively. We will get 1 when the peak is significant i.e. the peak should be above the threshold line else we will get the insignificant peak and that can be represented by 0. For example we had applied this technique on some randomly selected qechirec:- WY ite BRU CEE WY VEU EE EE We had applied the above mention technique on our database of American Sign Language which consists of 504 images i.e. 21 images per character and we was able to recognize 24 characters out of 26 characters from sign language because the remaining two characters gesture are dynamic and the developed approach is for static characters only. The threshold and codes we get are unique for each class of gestures, However if the output code will be the same for two class then there threshold will be unique. For example gesture B and M have generated same output code then there can be conflict but, then we can take threshold as our primary feature and classify them according to it because threshold will be unique. The outpu we get after LCS can be divided into two parts one which is representing figure region and the other one is representing the non figure region. We have to consider the figure region only to obtain peaks and generate codes. In the above outputs red line denotes the threshold line and the figure peaks above it can be taken into account. This threshold line plays an importan role by which we can decide which peak is significant and which is not. Significant peaks will be assigned value 1 and remaining 0 is assigned. There can be some false identification also which is shown in the result table. This false is due to the many reasons. Mainly i Se Nema wml. Chemnwce Texel scat secede on: Sac Back wil wm sen. mw berks mn lh leo elected les: ehvtodikk Mew Back acl mmm: “iMle>
iter if —_ FF Preece contour. That is every class of contour will have its unique threshold value and according to that threshold value we can able generate a unique code corresponding to that gesture class and that will be also unique for that class. That code can be generated by identifying the significant peaks and insignificant peaks from the contour and encode them from 0 and 1 respectively. We will get 1 when the peak is significant i.e. the peak should be above the threshold line else we will get the insignificant peak and that can be represented by 0. For example we had applied this technique on some randomly selected qechirec:- WY ite BRU CEE WY VEU EE EE We had applied the above mention technique on our database of American Sign Language which consists of 504 images i.e. 21 images per character and we was able to recognize 24 characters out of 26 characters from sign language because the remaining two characters gesture are dynamic and the developed approach is for static characters only. The threshold and codes we get are unique for each class of gestures, However if the output code will be the same for two class then there threshold will be unique. For example gesture B and M have generated same output code then there can be conflict but, then we can take threshold as our primary feature and classify them according to it because threshold will be unique. The outpu we get after LCS can be divided into two parts one which is representing figure region and the other one is representing the non figure region. We have to consider the figure region only to obtain peaks and generate codes. In the above outputs red line denotes the threshold line and the figure peaks above it can be taken into account. This threshold line plays an importan role by which we can decide which peak is significant and which is not. Significant peaks will be assigned value 1 and remaining 0 is assigned. There can be some false identification also which is shown in the result table. This false is due to the many reasons. Mainly i Se Nema wml. Chemnwce Texel scat secede on: Sac Back wil wm sen. mw berks mn lh leo elected les: ehvtodikk Mew Back acl mmm: “iMle>
Accuracy result after recognition of gesture
Accuracy result after recognition of gesture

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