Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

IET Image ProcessingVolume 15, Issue 5 p. 1166-1178 ORIGINAL RESEARCH PAPEROpen Access Segregation of meaningful strokes, a pre-requisite for self co-articulation removal in isolated dynamic gestures Anish Monsley K., Corresponding Author Anish Monsley K. anishmonsley@yahoo.com orcid.org/0000-0002-4927-3785 Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, India Correspondence Anish Monsley K., Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India. Email: anishmonsley@yahoo.comSearch for more papers by this authorKuldeep Singh Yadav, Kuldeep Singh Yadav orcid.org/0000-0002-4007-8502 Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, IndiaSearch for more papers by this authorSonghita Misra, Songhita Misra Department of Electronics and Communication Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, Andra Pradesh, IndiaSearch for more papers by this authorTaimoor Khan, Taimoor Khan Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, IndiaSearch for more papers by this authorM. K. Bhuyan, M. K. Bhuyan Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, IndiaSearch for more papers by this authorRabul Hussain Laskar, Rabul Hussain Laskar Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, IndiaSearch for more papers by this author Anish Monsley K., Corresponding Author Anish Monsley K. anishmonsley@yahoo.com orcid.org/0000-0002-4927-3785 Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, India Correspondence Anish Monsley K., Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, 788010, India. Email: anishmonsley@yahoo.comSearch for more papers by this authorKuldeep Singh Yadav, Kuldeep Singh Yadav orcid.org/0000-0002-4007-8502 Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, IndiaSearch for more papers by this authorSonghita Misra, Songhita Misra Department of Electronics and Communication Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, Andra Pradesh, IndiaSearch for more papers by this authorTaimoor Khan, Taimoor Khan Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, IndiaSearch for more papers by this authorM. K. Bhuyan, M. K. Bhuyan Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, IndiaSearch for more papers by this authorRabul Hussain Laskar, Rabul Hussain Laskar Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam, IndiaSearch for more papers by this author First published: 28 December 2020 https://doi.org/10.1049/ipr2.12095Citations: 2AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Gesture formation, a pre-processing step, has its importance when variations in patterns, scale, and speed come into play. Self co-articulations are intentional movements performed by an individual to complete a gesture, whose presence in the trajectory alters its original meaning. For recognition, most researchers have directly used the trajectory formed along with these self co-articulated strokes, with a few removing it using visible trait-like velocity. Usage of velocity has shortcomings as gesturing in air differs from gesturing over a solid surface; hence, we propose a gesture formation model, which incorporates global and local measures to remove these self co-articulations. The global measure uses Euclidean distance, instantaneous velocity, and polarity calculated from the complete gesture, while the local measure segments the gesture into stroke-level segments by using the minimum–maximum-polarity algorithm and applies the selective bypass rules. The proposed model, when experimented on gestures patterns with premeditated speed variation, has a mean error rate of 0.0069 and 7.40% self co-articulations;individuals’ natural gesticulation has a mean error rate of 0.0371 and 12.07% self co-articulations. Experimentation on each gesture of NITS hand gesture databases showed a relative improvement of 40% (accuracy 97%) over the existing baseline models. 1 INTRODUCTION Gesture is one of the ways to express a thought by bodily actions. An individual can perform a gesture as additional information to convey what they are trying to say. These gestures help particularly the disabled for whom gesturing may be the primary means of communication. In this regard, the research community has brought forward many developments, where the individual's speech or gesture movements can perform the intended actions through a human–computer interaction (HCI) machine without unnecessary physical movements. This work is a part of the research that focuses on using hand gesture movements to limit the usage of conventional input peripheral device such as keyboard and mouse. Hand gesture movements can be recorded by non-vision devices such as electrodes directly placed in hands, gloves, inertial sensor or through vision devices such as cameras and Kinect sensor. Though the former provides a more accurate estimation of the intended gesture, the vision-based approach has shown consistent growth in the field of hand gesture (in air) particularly character recognition [1, 2]. The act of gesturing in air includes with it some redundant strokes (pen-up strokes) that need to be removed to get the actual characters. These redundant strokes are intentional movements (ligature motion) made by an individual while gesturing to form the complete meaningful character. Researchers [3] called these redundant intentional movements within a gesture as self co-articulations, while co-articulation is made between two gestures [4, 5]. The number of self co-articulated (SCA) strokes varies with the individual, as a uni-stroke or multi-stroke gesture can be performed even without the usage of SCA strokes. Let us consider a keyboard character ‘A’. This can be gestured with minimum of ‘1–2’ SCA strokes, whereas character ‘B’ can be gestured with ‘0–1’ SCA strokes. Hence, like normal strokes, these SCA strokes are also subjective to the individual performing it. These strokes do not add/give any further information/meaning to a character, and the presence of SCA strokes may increase the complexity for recognising a gesture, e.g. ‘N’ gesticulated with self co-articulations (see Figure 1). Velocity can be used for segmentation [6, 7] of gesture into strokes and has been used by researchers to remove these SCA strokes [1-3]. FIGURE 1Open in figure viewerPowerPoint Gesture ‘N’ gesticulated with self co-articulations (Here 1,3,5 are normal stroke segments; 2,4 are SCA stroke segments) It was based on the hypothesis that individuals perform their gesture with self co-articulations at high speed. Gesturing in air varies from gesturing over the solid surface, as an individual performing a gesture (in air) will have SCA strokes with speed equal, greater, or sometimes less than that of normal strokes. Also, people with motion impairment, physically disabled, and elderly cannot perform the gesture with much intentional variation in speed. In these cases, velocity alone cannot be applied as the sole factor for removing these SCA strokes. This motivated us to investigate other distinguishing characteristics present in the gesture and explore new techniques to separate and remove these SCA strokes. The contribution of this paper includes the following: Extending the NITS hand gesture database [1] (for virtual keyboard entry) consisting of isolated dynamic gestures to 69 characters with a pattern, speed, and scale variations in the complex (static) background. Integration of global and local measures to remove self co-articulation within the gesture. Global (complete strokes)—Time-varying quantities such as displacement and instantaneous velocity are put together with time-independent factors such as polarity using a biased voting pattern to remove SCA strokes. Local (segmented strokes)—SCA stroke segments are bypassed by using a set of formulated selective bypass rules based on the number of stroke segments and the gesture's initial movements. Segmentation of the gesture into stroke-level segments is done by the proposed minimum-maximum-polarity (mMPo) algorithm for applying the selective bypass rules. The remainder of this paper is organised as follows. Section 2 briefs related works. Section 3 describes the developed database. Section 4 details about the proposed system model. Section 5 elaborates the results and discussion. Section 6 summarises the conclusion and highlights the future direction of work. 2 RELATED WORKS Gesture recognition is a broad term including, in itself, the following—gesture object identification, gesture formation, and gesture recognition. Researchers working in this area of gesture recognition focus either on the gesture object identification or on gesture recognition, as the input data is a static gesture (only spatial information) where an individual poses a gesture (identification + formation) and recognises it using conventional feature-based learning models or the deep architecture models. But in the case of dynamic gestures, gesturing is a time series of static frames (spatio-temporal information), and in it, gesture (trajectory) formation is also an important step before recognition. The works of literature reviewed below are analysed in aspect to object detection, data acquisition technique, gesture formation and input data used in their respective work. For gesture recognition, two researchers developed the NITS hand gesture database consisting of 40 characters, which were collected from 20 individuals. They used a red marker as a gesture object and a 2D camera for capturing the gestures. They calculated velocity between each frame and used the average velocity as a threshold to separate the normal strokes from SCA strokes. An improvement of ∼20% accuracy was attained when SCA strokes were detected and used as a feature to recognise each gesture [3]. Researchers detected bare hand in 2D HSV and 2D YCbCr colour space to form the gesture trajectory for isolated and continuous gestures made using numerals from 0 to 9 [8]. Twenty-two individuals were asked to gesture the 26 uppercase English alphabets and 10 words in a single continuous stroke. Based on the gesture motions, hidden Markov models (HMMs) for each character and HMMs with ligature motion for word level were developed to recognise them [9]. Utilising a sliding window with a step size of 10 frames, rules for detecting the gesturing in air events—writing and non-writing—were developed. For detection, seven features were extracted from both the events, and the Gaussian mixture model was used to classify. For recognition, 2D position and velocity were extracted from the detected frames, normalised, and then used as features for the HMM model [10]. To overcome the influence of speed variations in the gestures, researchers tried to alter the threshold velocity by including 50% of average velocity to it. Furthermore, to limit the distortions caused by the slight hand movements, frames with velocity lesser than 50% of the average velocity were also removed. These two steps helped mitigate the presence of SCA strokes to some extent [11, 12]. To make use of movements in the hand muscle for gesture recognition, researchers measured the muscle activity using electrodes during hand gesturing to form instantaneous high-density surface electromyography image. Databases such as CSL-HDEMG, NinaPro, and CapgMyo was used in their work. Statistics were calculated from the high-density scanning electron microscopic image to train the machine learning models for classification [13]. After designing a wireless inertial motion unit sensor, researchers [14] tried to capture the 3D handwritten gesture performed in a solid surface. Twenty volunteers were asked to gesture English alphabets (lowercase) and numbers from 0 to 9 in one direction as a single stroke. Words were separated from non-words using the device's inbuilt motions metrics such as acceleration, angular velocity, and 3D attitude (orientation). In [15], researchers normalised the speed variation using the dynamic time warping (DTW) technique. Then, they used Euclidean distance to match the trajectory points to a template, which was a trajectory formed from averaging every gesture trajectory points. Researchers in [16] detected fingertip through the 3D Kinect sensor and gave instructions as a static gesture to a virtual text keyboard with the assistance of an inbuilt flick keypad. In [17], 3D text gestured in the air with a single stroke was captured using a leap motion sensor. Then, researchers segmented these words using a sliding window with a heuristic search for finding the end of each word. In total, 320 sentences recorded from eight users were used for their work. Instead of forming a complete trajectory of the gesture, they [18] detected hand from each trajectory points of a frame with the help of principal component analysis, which reduced the dimensions. They used the MSR hand gestures dataset and the VIVA challenge dataset. Researchers [19] proposed the distance-weighted-curvature-entropy algorithm to detect and track the fingertip of a hand to form a complete trajectory of the gesture. 1800 gesture samples of numbers from 0 to 9 and 26 lowercase English alphabets were used for their work. Utilising the reflectance characteristics of a hand from an IR image emitted from a leap motion device, researchers [20] captured 16 static and four dynamic gestures. They used a combination of two-hand pose for representing dynamic gestures. Researchers [21] detected fingertip and captured the gesture and its trajectory using the Intel RealSense SR300 camera. The trajectory was normalised using nearest neighbour point and root point algorithms. RTD and 6MDG trajectory datasets were used for their work. In [22], they extended the work of [16] to detect hand in 2D HSV model space from a simple background. They had introduced seven new hand static gestures to control the virtual keyboard and to feed the input. To capture the motion data, researchers [23] made individuals to wear a wristband while gesticulating 26 English alphabets (Uppercase). The gesticulation act was based on the user-dependent model and the user-independent model. Accelerometer and gyroscopic features captured from the wrist band were used to train the DTW + k-nearest neighbours and convolutional neural networks for classification. Researchers [24] proposed a long short-term memory (LSTM)-based dynamic probability method for segmentation and recognition of static continuous gestures. The LSTM model was trained with invalid gestures (short and long trajectories) and valid gestures (Arabic numbers). Segmentation of the invalid sub-gestures was performed by merging only the input trajectory points with a trained model. The merger operation was based on the highest probability, i.e. the largest vector point of LSTM was taken as a confidence measure. Based on the number of SCA strokes in a gesture, researchers [25] developed a hierarchical model to distinguish 58 gestures (26 alphabets, 0–9 numbers, and 22 symbols). Then, trajectory-based and image-based features were extracted from the model and were used to train the Voronoi-diagram-based classifier and neuro-fuzzy for recognition. To sum up, researchers have used the gesturing objects like a fingertip, data gloves, electrodes, colour marker, hand, inertial motion sensors such as wireless-inertial-measurement unit, palm's GEOS-based devices, and wrist bands. Input acquisition devices such as a 3D-Kinect sensor, leap motion sensor, time-of-flight camera, infrared camera, stereo camera, and 2D camera were used to capture the gesturing act. Once gesture object is detected, they have either used the trajectory formed directly or matched it with a predefined template/character-set/graffiti/uni-stroke symbols by calculating the DTW distance or finding the absolute error/differential programming distance, the minimum of which is considered to form the gesture [26, 27]. This indicates that in both the cases, the pattern of the gesture is predefined/unitary in a single direction with fewer self co-articulations. Hence, these techniques would fall short when the gesticulating pattern for a gesture varies, involving directional changes and more self co-articulations to complete the gesture. Thus, the above techniques prevent the individual from using her/his inherent gesticulation style to gesticulate in the air for HCI. On the other hand, few researchers used velocity to remove these self co-articulations with a notion that these are of much higher speed. But when individuals gesture over the air, there is negligible speed variations between the normal and the SCA strokes unlike gesturing over the solid surface (pen-down and pen-up strokes) where there exist visible speed variations. Hence, when an individual gesticulates in the air with her/his innate gesticulation speed, velocity cannot be used to remove these self co-articulations. In all of these cases, researchers have used a minimal number of characters to demonstrate their work. Hence, there is a need to: a) develop a database with more number of characters which are gestured in a natural way incorporating the variations in pattern, scale, and speed; b) find an alternative solution to remove this self co-articulation from the conventional velocity-dependent and template matching techniques; and c) make the system model robust to variations in pattern and scale. In this work, we chose to use appearance-based gesture object, i.e. red colour marker, and a 2D camera for capturing the gesture in a complex environment and extend the NITS hand gesture database (for virtual keyboard entry). 3 DATABASE DEVELOPMENT In the NITS hand gesture database (for virtual keyboard entry), the volunteers with consent are asked to perform 95 keyboard characters, i.e. the isolated dynamic gestures. Each volunteer is instructed to perform the 95 gestures using a marker whose rear end is red coloured (size of 1 cm). Gesturing in the air should be within the purview of a 2D camera (Logitech 930e webcam with a frame rate (FR) of 30 fps) in a natural way they usually write. Thus, the gesturing act has no restraint on: i) gesture pattern; ii) speed of gesticulation; and iii) scale of gesture. The volunteers were asked to perform again if their gesture was not recorded properly. Each gesture was saved as a separate video with a filename, e.g. ‘Re0001M001N00001’. In this nomenclature, position ‘1-2’ indicates red colour marker, position ‘3-6’ indicates volunteer's identity number, position ‘7’ refers to the gender, position ‘8-10’ indicates the gesture number, position ‘11’ A/N/S indicates what is that gesture, i.e. alphabets/numbers/symbols, and position ‘12-16’ indicates the video number. Details of the 95 gestures are (26 English alphabets in capital, 26 English alphabets in smaller, 10 Numbers (0–9), 33 Symbols (addition ‘+’, subtraction ‘–’, multiplication ‘*’, division ‘÷’, tilde ‘∼’, at sign ‘@’, equal to ‘=’, caret ‘^’, ampersand ‘&’, left curly bracket ‘{’, right curly bracket ‘}’, left square bracket ‘[’, and right square bracket ‘]’, left round bracket ‘(’, right round bracket ‘)’, backslash ‘∖’, question mark ‘?’, percentage ‘%’, lesser than ‘<’, greater than ‘>’, dollar sign ‘$’, exclamation sign ‘!’, slash ‘/’, dot ‘.’, comma ‘,’, colon ‘:’, semi-colon ‘;’, hash ‘#’, left arrow ‘↽, right arrow ‘⇁, top arrow ‘↾, bottom arrow ‘⇂’, and backspace ‘←’). The database at present has 5320 gestures videos collected from 56 volunteers—NITS (45 males and 10 females), of which 3 are left-handed and ‘1’ male is from general public. In this research work, we will use 69 characters leaving out the lowercase English alphabets. 4 PROPOSED METHOD In this research work, all the gesture videos are pre-processed using open-source video editing software. This is performed to make the red coloured marker, which is detected in the first frame be the starting coordinate point and the last frame be the last coordinate point for the gesture. 4.1 Red marker detection The first step of gesture identification is to detect the red coloured marker, for which we have followed an approach similar to [12]. All the red coloured objects are first detected from each frame by subtracting the green (Ig) and the blue channel (Ib) from the red channel (Ir) using (1). Then, to obtain the luminance of the red coloured objects, the red channel is subtracted from its grey channel (GR) by (2). Finally, using (3), the Otsu threshold minimises within-class and maximises between-class variance and groups them [28], which would result in obtaining our desired region of interest (ROI) (red coloured marker). This ROI's centroid coordinate is detected in each frame using (4), which would be tracked to form the complete gesture [see Figure 2(a)]. Then, each coordinate point is checked with its neighbouring coordinates; if the difference of them exceeds a limit, we replace these coordinates with its immediate succeeding coordinate subtracted from a random integer through (5) [see Figure 2(b)]. This will make the gesture to be in its intended path. By experimentation, the limit of 50 was chosen, as it worked for all the different scales used for gesturing. The trajectory is further smoothened (SM) by averaging with its immediate neighbours by (6) [see Figure 2(c)]. Figure 2 illustrates the working of (4)–(6) for a gesture ‘F’ R ( i , j ) = I r i , j − I g i , j + I b i , j (1) where i = 1 to N ; j = 1 to M . L i , j = R i , j − G R i , j (2) where G R ( i , j ) = 0.299 ∗ R ( i , j ) . O i , j = L i , j ε M a x σ B k w h e r e σ B k = μ 1 − μ 2 2 ω 1 1 − ω 2 (3) C E x = ∑ p = a b i p b ; C E y = ∑ q = a b j q b (4) where a and b are the coordinates of the ROI . R M k , l = C E x Fr ; C E y Fr ∈ if C E x Fr + 1 − C E x Fr < 50 C E y Fr + 1 − C E y Fr < 50 C E x Fr + 1 − r a n d ; C E y Fr + 1 − r a n d ∈ if C E x Fr + 1 − C E x Fr > 50 C E y Fr + 1 − C E y Fr > 50 (5) FIGURE 2Open in figure viewerPowerPoint Gesture ‘F’ gesticulated with self co-articulation. (a) Input trajectory. (b) Corrected trajectory. (c) Smoothened trajectory where Fr = 1 to total number of frames . S M X , Y = R M k − 1 + R M k + R M k + 1 3 R M l − 1 + R M l + R M l + 1 3 (6) w h e r e k = 2 to F r − 1 ; l = 2 to F r − 1 ; X = 1 to F r − 2 ; Y = 1 to F r − 2 . 4.2 Removal of SCA stroke Self co-articulations in gesture are a part of intentional movements that are needed to complete the gesture. The red marker detection in each frame will include these self co-articulations, which can distort the actual intended meaning of the character (see Figures 1 and 2). Statistical analysis on the trajectory points revealed that the number of coordinate points and the distance between successive coordinate points vary in the normal and SCA stroke; instantaneous velocity (metre/second) can be formulated using pixel density (pixel per inch—PPI), scale information, and distinct polarity observance from the gradient of strokes. Visual observation of gesticulating act showed that, if the gesture stroke does not begin at its conventional ground truth (GT) position, there is a natural tendency to make use of self co-articulations every second time to complete the gesture. However, to make use of this proposition, the trajectory must be segmented into strokes segments, for which an mMPo algorithm is proposed. Based on the number of segmented strokes segments and initial gesture movements, selective bypass rules (hard threshold) have been formed. Integration of two approaches is being utilised for the removal of SCA strokes, and the proposed system model is shown in Figure 3. FIGURE 3Open in figure viewerPowerPoint Proposed gesture formation model for self co-articulation removal Euclidean distance (ED): Displacement is the change in position of vectors; this is one of the primitive visible characteristics that can be obtained from the trajectory points. To measure the distance of each successive 2D coordinate points in the trajectory, using (7), the Euclidean distance is calculated. This is one of the commonly used distance measures in gesture recognition E D n o = S M X + 1 − S M X 2 + S M Y + 1 − S M Y 2 (7) where n o = 1 to F r − 2 ; X = 1 to F r − 2 ; Y = 1 to F r − 2 . Here, n o represents the frame of the corresponding coordinates points. For example, frame n o = 1 corresponds to X = 1 and Y = 1. Instantaneous velocity (IVe): Instantaneous velocity per frame (metre/second) is calculated using the displacement of the trajectory points (pixels/frame), the frame rate of video (frame/second), and scale (metre/pixel). Pixel density (PD) was used to calculate the scale information I D x d n o = S M X + 1 − S M X 2 (8) I D y d n o = S M Y + 1 − S M Y 2 (9) FR = Number of frames / second (10) P D inches = W 2 + H 2 D (11) P D metre = P D inches ∗ 39.3701 (12) Scale = 1 / P D metre (13) I V x d n o = I D x d n o ∗ FR ∗ Scale (14) I V y d n o = I D y d n o ∗ FR ∗ Scale (15) T V x y n o = E D n o ∗ FR ∗ Scale (16) where n o = 1 to Fr − 2 ; X = 1 to Fr − 2 ; Y = 1 to Fr − 2 . The subscripts x d , y d , and x y represent the coordinate points of x-axis, y-axis, and both x-axis and y-axis. The FR represents the number of frames per second in the input video (FR = ∼30 fps). Here, W , H , and D represent the width, height, and diagonal length of the display unit. The constant 39.3701 corresponds to metric of PPI to pixel per metre conversion. Polarity (Po): Analysis of the gradient of each consecutive 2D coordinate points revealed that some of the normal strokes and the SCA strokes are of opposite polarity. We used the gesture initial movements (horizontal/vertical) information to choose the polarity that corresponds to the respective strokes by using (19). This concept is similar to the zero-crossing rate of speech signal processing [29] P x d n o = S M X + 1 − S M X (17) P y d n o = S M Y + 1 − S M Y (18) P S = P x d > 0 i f S M Y 1 − S M Y M < S M X 1 − S M X M P y d 0 i f S M Y 1 − S M Y M S M X 1 − S M X M (19) where X = 1 : Fr − 2 ; Y = 1 to Fr − 2 ; n o = 1 to Fr − 2 . The variable 'P' is the output from the gradient operation along the x-axis ( P x d ) and along the y-axis ( P y d ). ‘P’ would consist of coordinate points from the normal stroke segments. The value of M is chosen to be 10/15 depending upon the scale of the gesture. To use polarity for removing self co-articulation, the original starting and ending coordinate point of the gesture is again kept as start and end coordinate point along with P S coordinates. In some cases, removal of the SCA stroke resulted in either removing the first stroke or last stroke, as they were in the same polarity. The above-mentioned condition will help us overcome this limitation of polarity at the time of gesture formation. Thus, the resultant formed characteristic ‘Po’ is invariant to the pattern, as for each pattern the consecutive polarity would also change depending on the initial gesture movements. As it depends on the spatial coordinates only, dependence on velocity can be minimised. In the case of scale variation, the polarity would remain the same irrespective of the size. Thus, polarity will support other visible characteristics like velocity and displacement for the detection and removal of self co-articulations. Biased voting: Time-dependent characteristics such as Euclidean distance and instantaneous velocity and time-independent characteristics such as polarity are allotted with equal weights of 0.5. A biased vote is cast to make the system model less dependent on speed variation of the gesticulating pattern. A coordinate point to get selected needs a threshold greater 0.5 suggesting that all candidates must necessarily satisfy the polarity criteria. It was found experimentally