Visual Multi-touch Air Interface for Barehanded Users ... - Springer Link

International Journal of Control, Automation, and Systems (2013) 11(1):84-91 DOI 10.1007/s12555-012-9217-y

ISSN:1598-6446 eISSN:2005-4092 http://www.springer.com/12555

Visual Multi-touch Air Interface for Barehanded Users by Skeleton Models of Hand Regions Jin Gyu Kim and Young Hoon Joo* Abstract: In this paper, we propose a visual multi-touch air interface. The proposed air interface is based on the image processing and does not require any additional equipment except a built-in camera. The implemented device provides a barehanded interface which copes with the multi-touch operation. The proposed device is easy to apply to the real-time systems because of its low computational load and is cheaper than the existing methods using glove data or 3-dimensional data because any additional equipment is not required. To improve the robustness of the extraction of hand regions under various circumstances, we propose an image processing algorithm based on the HCbCr color model, the fuzzy color filter, and the labeling. In addition, to improve the accuracy of the recognition of hand gestures, we propose a motion recognition algorithm based on the geometric feature points, the skeleton model, and the Kalman filter. Finally, the experiments show that the proposed device is applicable to remote controllers for video games, smart TVs and any computer applications. Keywords: HCbCr model, multi-touch operation, skeleton model, visual multi-touch air interface.

1. INTRODUCTION The purpose of Human-Computer Interaction (HCI) is to improve the interface between users and computers so that users can utilize computers conveniently and usefully. Recently, the needs for an intuitive and effective HCI is increasing because of the wide use of information communication devices, multimedia contents, video games and the development of thin flat panel displays [1-4]. The HCI devices are divided into touch and non-touch devices. The touch-based devices are suitable to operate or control the mobile devices such as smart phones, tablet PCs and mobile internet devices (MID). But such devices need an additional pressure or electrostatic touch-sensor panels to recognize touch operation [4,5] which involves cost increases. In Addition, the touchbased devices restrict the action radius of users because the users have to touch a monitor or a screen to operate the devices [6-8]. To overcome the disadvantages of the touch-based devices, many researchers have devoted themselves to the non-touch-based devices called the air interface. The __________ Manuscript received December 14, 2011; revised June 12, 2012; accepted July 27, 2012. Recommended by Editorial Board member Pinhas Ben-Tzvi under the direction of Editor Myotaeg Lim. This work was supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economy (No. 20124010203240). Jin Gyu Kim and Young Hoon Joo are with the Department of Control and Robotics Engineering, Kunsan University, Kunsan, Chonbuk 573-701, Korea (e-mails: {kjk3242, yhjoo}@kunsan. ac.kr). * Corresponding author. © ICROS, KIEE and Springer 2013

air interface is the radio-based communication link between the mobile station and the base station, which is familiar to the public by virtue of several science fiction films. Most of the air interfaces can be categorized as the glove data method, the 3-dimensional model method and the 2-dimensional pattern method. The glove data method which uses the gloves equipped with sensors to recognize the position and the gesture of hands has the best performance, but increases the cost and causes inconvenience in wearing gloves [9]. The 3-dimensional model method also requires additional equipment such as stereo vision devices which provide depth information [10-12]. Another 3-dimensional method without additional equipment reanalyzes 2-dimensional information into 3-dimension, which is not suitable to implement the real time system because of its high computational load [13]. On the other hand, the 2-dimensional pattern method provides the cheapest and simplest solution, but this method is so sensitive to changes in brightness and skin colors, which causes frequent errors in the procedure of extracting hand regions. What is worse, it is hard to recognize hand gestures accurately because 2dimensional information does not provide sufficient data [14,15]. In this paper, we propose a visual multi-touch air interface based on 2-dimensional pattern information. The proposed interface is developed for a remotebarehanded user so that it does not need any additional equipment such as touch-sensor panels, sensor gloves, and stereo cameras. Thus, the interface can be easily adopted as a convenient remote-control device for console games, smart TVs, and computer applications. The proposed method consists of the robust extraction of hand regions and the accurate recognition of hand gestures. The part of extracting hand regions is carried out as follows: The HCbCr color model is proposed to

Visual Multi-touch Air Interface for Barehanded Users by Skeleton Models of Hand Regions

reduce an adverse effect caused by changes in brightness when skin colors are extracted from the input image. Also, the fuzzy color filter is employed to minimize errors caused by differences between skin colors. And, the labeling algorithm is used to distinguish hand regions from skin colors. The part of recognizing hand gestures is carried out as follows: The skeleton model is generated by using the silhouette and the feature points of hands. The hand gestures are recognized by measuring the angles between joints and the lengths of knuckles. And, the Kalman filter is used to track the hand movement smoothly. The proposed visual multi-touch air interface can reduce the production costs because it does not require any additional equipment, be easily applied to any existing system, and increase convenience of users because of the accuracy and the robustness against the environment. Finally, the experiment results show the applicability and the practicality of the proposed interface. 2. OVERVIEW OF THE PROPOSED SYSTEM The visual multi-touch air interface proposed in this paper consists of the extraction of hand regions and the recognition of hand gestures as shown in Fig. 1.

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The extraction of hand regions is performed by the HCbCr color model, the fuzzy color filter, the morphology, and the labeling. The skin color regions are extracted from normalized input images by means of the HCbCr color model and the fuzzy color filter. The proposed HCbCr color model and the fuzzy color filter can cope with the change in brightness and the difference between various skin colors. The morphology is used to eliminate noises generated in the process of binarization of the images. The labeling is used to distinguish hands from the extracted skin color regions which include faces as well as hands. The recognition of hand gestures is performed through the process of feature point detection, skeleton model, and motion recognition. The feature point detection finds out the center of a hand, the silhouette of a hand, and the end points of fingers by means of the center of gravity and the curvature method. The skeleton model is used to find the locations of each finger joint by matching the feature points with this model. The motion of fingertips is recognized by means of measuring the angles of joints and the lengths of knuckles. At this time, the Kalman filter is used to track the hand movement smoothly. 3. EXTRACTION OF HAND REGIONS As mentioned in the previous section, the extraction of hand regions is carried out by the RGB normalization, the HCbCr color model, the fuzzy color filter, the morphology, and the labeling. Among these procedures, this section deals with main procedures in detail such as HCbCr color model, fuzzy color filter, and labeling. 3.1. HCbCr color model The regions with skin colors are distinguished from other regions with non-skin colors by an appropriate color model because the skin colors are distributed over a specified space on the whole color space. This is the reason why the selection of a color space plays a crucial role in success of extracting skin colors. In general, the HSI and the YCbCr color spaces are used to extract skin colors, but these color spaces are very sensitive to the change in brightness. In this paper, we propose a new color model robust against such change. Figs. 2 and 3 show the distribution of skin colors on the HSI and the YCbCr color spaces according to brightness. As shown in Fig. 2, the H component among HSI components is concentrated most but changed least according to brightness. And, as shown in Fig. 3, the Y

Fig. 1. The structure of the proposed visual multi-touch air interface.

(a) Under 300 [lx].

(b) Under 600 [lx].

Fig. 2. Histogram of skin colors on HSI color space.

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3.2. Fuzzy color filter (FCF) The proposed HCbCr color model has a property robust against the change in brightness, but is not enough to cope with the differences between various skin colors. To supplement this, we add the fuzzy color filter which is designed based on the actual samples of skin colors [17-20]. The rule of the fuzzy color filter is given in (3).

(a) Under 300 [lx].

(b) Under 600 [lx].

Fig. 3. Histogram of skin colors on YCbCr color space.

IF H is M i1 and Cb is M i 2 and Cr is M i 3 , THEN zi ( X ) = ai ,

(3)

where Mi1, Mi2, and Mi3 are the Gaussian membership functions according to each color, zi(X) is the ith output rule at the pixel position X. The intermediate output I(X) is determined like (4). n

(a) Under 300 [lx].

I(X ) =

(b) Under 600 [lx].

Fig. 4. Histogram of skin colors on HCbCr color space. component among YCbCr components is concentrated least but changed most according to brightness. Therefore, a new color space consisting of H, Cb, and Cr components is introduced to extract the skin colors. The histogram of skin colors on the proposed HCbCr color space has similar distributions despite of the change in brightness as shown in Fig. 4. Based on the above observation, we propose a new HCbCr color model whose color components are determined like (1). ⎡ ⎤ 2R − G − B ⎥, h = cos −1 ⎢ ⎢ 2 ( R − G ) 2 + ( R − G )(G − B ) ⎥ ⎣ ⎦ 180h H= ,

(1)

π

Cb = −0.1687 R − 0.3313G + 0.5000 B, Cr = 0.5000 R − 0.4187G − 0.0813B,

where R, G, and B are the color values of the normalized RGB color model of an input image. In this paper, we use the skin color ranges of each component of the HCbCr color model as (2). These ranges are determined by analyzing the sample images [16]. Therefore, these ranges may be little different according to sample images. 0 < H < 20, 77 < Cb < 127, 133 < Cr < 173.

(b) Skin color region.

Fig. 5. Skin color regions extracted by HCbCr color.

3

⎞

i =1 ⎝ j =1

⎠ , ⎛ 3 ⎞ ∑ ⎜⎜ ∏ μM ij ( X j ) ⎟⎟ i =1 ⎝ j =1 ⎠

(4)

n

where ∏ μ M ij ( X j ) denotes the fitness of the rules. Finally, the output of the fuzzy color filter at each pixel Iˆ( X ) is given as the decision function (5). Iˆ( X ) = γμ ( I ( X ) − I min ),

(5)

where γ is an offset value for gray image, which is a design parameter to determine skin colors. The fuzzy color filter is identified by a genetic algorithm which is coded by the average, the variance, and the conditional coefficient of the Gaussian membership function. For this, the cost function F is defined as follows. F = eskin + ebg ,

(6)

where eskin is the error when an input is a skin color, and ebg is the error when an input is a back ground color. The samples of skin colors for identification of the fuzzy color filter are shown in Fig. 6. The membership functions used in the identification, the input image and the output image of the fuzzy color filter are shown in Fig. 7, respectively.

(2)

Fig. 5 shows the extracted regions of skin colors by using the proposed HCbCr color model.

(a) Input image.

⎛

∑ ⎜⎜ ∏ μM ij ( X j ) ⎟⎟ ai

Fig. 6. The samples of skin colors.

(a) Membership (b) Input image. function.

(c) Regions of skin colors.

Fig. 7. The extracted regions of skin colors by the fuzzy color filter.

Visual Multi-touch Air Interface for Barehanded Users by Skeleton Models of Hand Regions

(a) Regions of skin colors.

(b) Region of hand.

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(a) Detected fingertips. (b) Center of hand and fingertips.

Fig. 8. The extracted region of hand by labeling.

Fig. 9. Feature points of the hand region.

The results of the HCbCr color model and the fuzzy color filter are gray images, and these images are binarized into a black and white image. The noises generated in this procedure are eliminated by the erosion and the dilation operations of the morphology. The erosion operation of the morphology removes noises spread irregularly, and the dilation operation of the morphology recovers the loss of the extracted regions in the procedure of the erosion [20].

X = [ x, y ]T . The center of gravity of the hand region is given as

3.3. Labeling The extracted regions of skin colors in the above section include faces as well as hands. In most cases, a user brings his hand closer to a camera than his face when he operates a computer, that is, the largest one among regions of skin colors is most likely to be the hand. Therefore, to extract the region only by the operating hand, it is required to recognize geometrically connected regions of skin colors as objects and to select the largest one. To achieve this, the 8-directional labeling scheme is employed [22]. Fig. 8 shows the labeling result selecting the hand region among the regions of skin colors. 4. RECOGNITION OF HAND GESTURES

As mentioned in the Section 2, the recognition of hand gestures is carried out through the process of center of gravity, curvature method, feature point detection, skeleton model, and motion recognition and tracking. Among these procedures, this section deals with main procedures in detail such as feature point detection, skeleton model, and motion recognition and tracking.

xc =

M 01 M , yc = 10 . M 00 M 00

(8)

Meanwhile, the angle between consecutive points on the silhouette of the hand region is obtained by

θ ( pi ) =

pi pi − k i pi pi + k pi pi − k

pi pi + k

,

where pi is the ith point on the silhouette, pipj is the vector with the initial point as pi and the end point as pj, k is an integer from 5 to 25, and i denotes the inner product . If the angle θ in (9) is larger than 180 degree, the corresponding point pi becomes the candidate of one of fingertips. The actual fingertips are detected by grouping these candidates [23]. Fig. 9 shows the resulting feature points which consist of the center and the fingertips of the hand. 4.2. Skeleton model generation In this paper, the skeleton model as shown in Fig. 10 is used to find the final feature points of the hand region. The skeleton model provides a vector presentation of the main joints by means of the database of the physical structure of the human hand. The center and fingertips detected in the previous section are matched to the skeleton model.

4.1. Feature point detection The feature points of the hand region extracted in the previous section are detected by using the center of gravity and the curvature method. To find the center of gravity of the hand region, it is required to obtain the area of the hand M00, the 1st moments of each coordinate M01, and M10 as follows. M 00 = ∑ ∑ Iˆ( x, y ), x y

M 01 = ∑ ∑ xIˆ( x, y ), x y

(7)

M 10 = ∑ ∑ yIˆ( x, y ), x y

where Iˆ( x, y ) is the output value at corresponding pixel

(9)

Fig. 10. Skeleton model of the human hands.

Jin Gyu Kim and Young Hoon Joo

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(a) Skeleton model generation.

(b) Output image.

Fig. 11. Final feature points generated by the skeleton model matching. In this figure, F1 is the center of the hand, and F12 to F16 are the fingertips. The positions of other feature points corresponding to the knuckle joints are obtained by the ratio information of the skeleton model as follows. These ratios may also be little different according to the database of the physical structure of the human hand. 27 15.6 hw , S3 = S4 = S5 = hw , 54 54 15 18 12 S6 = S8 = hw , S7 = hw , S9 = S10 = hw , 54 54 54 (10) 30 33 25.8 S11 = hw , S12 = S13 = hw , S14 = hw , 54 54 54 20.4 S15 = hw , 54 S1 = S2 =

where hw is the width of the hand. Fig. 11 shows the final feature points of the hand obtained by using the skeleton model matching. 4.3. Motion recognition and tracking The skeleton model generated in the previous section is used to recognize the operating motion such as clicking a button. Each finger of the skeleton model is represented as 3 feature points and the operating motion is detected by measuring the changes in the angle between the feature points as shown in Fig. 12. At this time, the clicking point is the corresponding fingertip. On the other hand, the final feature points are moved continuously while a user operates computer application. It can be happen to fail in detecting the final feature points when the user moves his hand very fast or the hand overlaps the face. The only detection of the final

Fig. 12. Motion recognition by measuring the angle of feature points.

Fig. 13. Kalman filter algorithm. feature points on each frame of input images is not sufficient to cope with these situations. Thus, it is required to estimate the hand motion between frames and the final feature points of the failed frame. In this paper, the missed positions of current feature points are estimated based on the measured position of previous ones by means of the Kalman filter [24]. Fig. 13 shows the Kalman filter algorithm used in this paper. In order to apply a Kalman filter, we assume that the movement of the hand position is sufficiently small during a step interval ∆t. Accordingly, a dynamic process can be used to describe the x or y coordinate of the center of the hand on the image plane with the state vector x which includes it’s position and velocity. The dynamic process is defined as follows: xk +1 = Ak xk + Gk wk ,

(11)

where ⎛ Δt 2 ⎞ ⎛ 1 Δt ⎞ ⎜ 2 ⎟. G = Ak = ⎜ , ⎟ k ⎜ Δt ⎟ ⎝0 1 ⎠ ⎝ ⎠

The system noise is modeled by wk, unknown scalar acceleration. An observation model is given by zk +1 = Hxk + vk ,

(12)

where H = [1,0], xk +1 is the actual state vector at time k +1, v is measurement noise, and zk +1 is the observed location at time k +1. The noise covariances are determined by experiments so that the system can perform feature point optimal tracking. 5. EXPERIMENT RESULTS

We carried out an experiment to verify the performance of the proposed air interface system. The specifications of the system are PC with Quad-core 3.3GHz CPU and 8GB RAM, and camera with the size of 320×240 pixels and 12 frames/sec. Fig. 14 shows the implementation procedure sequentially. Fig. 14(a) is the input image, (b) is the extracted regions of skin colors by using the HCbCr color model and the fuzzy color filter, (c) is the hand region by using labeling, (d) is the silhouette and the fingertips by using the center of gravity and the curvature method, (e) is the skeleton model consisting of the final feature points, and (f) is the output image.

Visual Multi-touch Air Interface for Barehanded Users by Skeleton Models of Hand Regions

(a) Input image.

(b) Regions of skin colors.

29 Frame.

79 Frame.

(c) Hand region.

(d) Silhouette and fingertips.

103 Frame.

121 Frame.

(e) Skeleton model.

(f) Output image.

186 Frame.

247 Frame.

Fig. 14. Implementation procedure of the proposed nontouch input method.

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Fig. 16. Recognition experiments of click operation while tracking the trace of detected fingertips.

(a) Under 270 [lx]. (b) Under 400 [lx]. (c) Under 600 [lx]. Fig. 15. Input images with various brightness.

36 Frame.

79 Frame.

172 Frame.

258 Frame.

427 Frame.

830 Frame.

Table 1. The success rates of extracting feature points. Total frames Successful frame (HCbcr) Successful frame (HCbcr+FCF) Successful frames (HIS) Successful frames (HIS+FCF) Successful frames (YCbCr) Successful frames (YCbCr+FCF)

270 [lx] 550

400 [lx] 610

600 [lx] 570

494(89.8%)

550(90%)

520(91.2%)

517(94%)

581(95.2%)

548(96.1%)

464(84.4%)

528(87%)

502(88%)

481(87.5%)

546(89.5%)

517(90.7%)

475(86.3%)

540(88.5%)

511(89.6%)

490(89.1%)

572(93.6%)

531(93.2%)

The proposed method has a property robust against the environment especially the change in brightness. Under the various brightness as shown in Fig. 15, the success rates of extracting feature points shown in Table 1. Figs. 16 and 17 are the experiment results that the proposed visual multi-touch air interface is applied to operating a computer application. The 29th frame in Fig.

Fig. 17. Trajectories of fingertips drawn by the proposed interface. 16 shows the detected fingertips denoted by red spots and the 79th frame shows the detected hand center denoted by the yellow circle.

Jin Gyu Kim and Young Hoon Joo

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[1]

[2]

[3]

Fig. 18. Changeover of window operated by the proposed interface. These fingertips play the role of multi-pointers of a mouse, which can cope with the multi-touch function provided by recent computer operating systems. The 103th and the 121th frames show the bending motion recognition of knuckles, where the motions of the index finger, the middle one, and the ring one correspond to click operations. Once the fingertips activated by clicking, the trajectories of the fingertips are displayed by various colors as shown in the 186th frame. When the user stretches out his fingers again, the clicking is deactivated as shown in the 247th frame. Fig. 17 shows the example of selecting colors and drawing figures by using the proposed visual multi-touch air interface. Fig. 18 shows the example of operating a computer application. The fingertips play the role of the multiple mouse pointers and the click event is recognized by the bending motion of knuckles. At this point, each finger can be assigned as various commands such as basic click, navigating forward/backward and scroll up/down.

[4]

[5] [6]

[7]

[8]

6. CONCLUSIONS

This paper dealt with the visual multi-touch air interface by using the skeleton model of hands. To extract the region of the hand robustly, we proposed the HCbCr color model to cope with the change in brightness and the fuzzy color filter to cope with the difference between skin colors. And, to recognize the gestures of the hand exactly, we employed the skeleton model to detect the feature points of the hand and the Kalman filter to track the movement of the hand. The proposed method can be easily applied to the existing systems because it does not require any additional equipment and does not burden the system with high computational loads. The method also provides a comfortable computing environment for the users because of its robustness and accuracy. The experiments operating a computer application have validated the applicability and the practicality of the proposed method. The proposed visual multi-touch air interface can be easily adopted as a convenient remote controller for mobile devices, smart TVs, console games, and presentation solutions as well as any PC application.

[9]

[10]

[11]

[12]

[13]

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Visual Multi-touch Air Interface for Barehanded Users by Skeleton Models of Hand Regions

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Jin Gyu Kim received his B.S. and M.S. degrees in the School of Electronics and Information Engineering from Kunsan National University, Kunsan, Korea, in 2007 and 2009, respectively. He is currently working toward a Ph.D. degree. His research interests include humanrobot interaction and intelligent surveillance systems. Young Hoon Joo received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Yonsei University, Seoul, Korea, in 1982, 1984, and 1995, respectively. He worked with Samsung Electronics Company, Seoul, Korea, from 1986 to 1995, as a project manager. He was with the University of Houston, Houston, TX, from 1998 to 1999, as a visiting professor in the Department of Electrical and Computer Engineering. He is currently a professor in the Department of Control and Robotics Engineering, Kunsan National University, Korea. His major interest is mainly in the field of intelligent robot, intelligent control, human-robot interaction, and intelligent surveillance systems. He served as President for Korea Institute of Intelligent Systems (KIIS) (2008-2009) and is serving as Editor for the International Journal of Control, Automation, and Systems (IJCAS) (2008-present), and is serving as the VicePresident for the Korean Institute of Electrical Engineers (KIEE) (2012-present).