Hand Gesture Recognition for Kinect v2 Sensor in the Near Distance Where Depth Data Are Not Provided

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1 , pp Hand Gesture Recognition for Kinect v2 Sensor in the Near Distance Where Depth Data Are Not Provided Min-Soo Kim 1 and Choong Ho Lee 2 1 Dept. of Info. and Comm. Eng., Hanbat National Univ., Daejeon-City, Rep. of Korea 2 Graduate School of Info. and Comm. Eng., Hanbat National Univ., Daejeon-City, Rep. of Korea 1 asdq200@naver.com, 2 chlee@hanbat.ac.kr Abstract Kinect v2 sensor does not provide depth information and skeletal traction function in near distance from the sensor. That is why many researches, to recognize hand gestures, are focused on the skeletal tracking only inside the range of detection. This paper proposes a method which can recognize hand gestures in the distance less than 0.5 meter without conventional skeletal tracking when Kinect v2 sensor is used. The proposed method does not use the information of depth sensor and infrared sensor, but detect hand area and count the number of isolated areas which are generated by drawing a circle in the center of the hand area. This method introduces new detectable gestures without high cost, so that it can be a substitute for the existing mouse-movement controlling and dynamic gesture recognition method such as clicking a mouse, clicking and dragging, rotating an image with two hands, and scaling an image with two hands in near distance. The gestures are appropriate for the user interface of smart devices which employ the interactions based on hand gestures in near distance. Keywords: User interface, Kinect v2 sensor, Hand gesture recognition 1. Introduction In recent years, hand gesture recognition has been actively studied as one of the human computer interactions. Since the gesture recognition can be used for various kinds of digital devices such as smartphones, tablet computers as well as conventional desktop computers and laptop PCs, much of the attention is concentrated on the related works. [1] To detect hand area, the existing method uses various color models such as YCbCr, HSV and RGB. They determine thresholds considering illumination and background objects included in the environments, but they are not consistent and very sensitive to various environmental factors. Since the colors of face area and other skin area are very similar to the hand color, it is very difficult to determine the hand area by color information. For actual application to detect hand area, the existing method shows low performance to discriminate hand area from face area when hand area is overlapped with face area. Further, in order to improve detection performance, it is necessary to wear specific colored gloves [2-4], or uses only depth information without infrared information [5-7]. Without using the color information and special gloves, finger tracking methods are generally used such as in [5, 8, 9]. But these kinds of methods use relatively complicated algorithms [9, 10] such as SVM or Convex Hull or AdaBoost. Moreover, designing hand gesture recognition without high cost has become another important issue and described in [11]. A face recognition technique using Kinect is reported in [12]. On the other hand, for the applications, various sensors are introduced in the market. There are two kinds of sensors that are commonly used. One is used for short distance such as from 0.2 to 1.2 meters and the others are used for relatively long distance for ISSN: IJSEIA Copyright c 2016 SERSC

2 example from 0.8 to 4.0 meters, or from 0.5 to 8.0 meters. These pertain to Kinect v1 and v2 sensor of Microsoft [13] and that one pertains to Intel Realsense SR300 [14]. Further, two kinds of sensors provide the skeletal recognition for hand gesture recognition, and uses depth information in the available distance. However, Kinect v2 sensor neither provides depth information nor detects infrared information in the distance less than 0.5meters. Further, the recognition method in Intel Realsense needs complicated conventional skeletal recognition. Figure 1 shows the depth sensor and IR (infrared sensor) of Kinect v2 sensor. The range of depth sensor and IR sensor of Kinect v2 sensor is 0.5 meter ~ 8.0 meters while it is 0.8 meter ~ 4.0 meters for Kinect v1. As Kinect sensors are developed, many research studies are conducted to recognize human movement activities using the sensors. [9, 15-21] A technique to improve Kinect-skeleton estimation is reported in [15]. To correct radial distortion of RGB camera and find the transformation matrix for the correspondence between the RGB and depth image of Kinect v2 is described in [16]. Intuitive hand gestures for controlling the rotation of 3D digital object is described in [19]. The conventional method which extracts palm region using RGB and depth sensor has been reported by [19]. In addition to that, tracking and evaluation of human motion for medical application is in [20], a depth completion method using Kinect v2 is in [17]. When emphasis is on the real time using Kinect v2 sensor, it is reported in [9, 21]. Figure 1. Kinect for Windows v2 Sensor which has a Color Camera, a Depth Sensor and an IR Sensor However, no research has been reported to utilize Kinect v2 in the near distance outside the detection range but almost all of the research studies were conducted inside the detection range. This paper presents a new simple method to recognize hand gestures in the distance less than 0.5 meter for Kinect v2 sensor without using the data of depth sensor and IR sensor. In other words, this method enables Kinect v2 sensor to detect hand area and recognize hand gestures in the near distance less than 0.5 meter where it does not provide related depth information or infrared information or skeletal recognition method. Furthermore, the newly developed simple gestures will also be presented. This paper extends the research which is described in [22]. Section 2 explains the method of hand area detection which we come up with to detect the hand area in the near distance where the depth sensor cannot give depth information. 408 Copyright c 2016 SERSC

3 Section 3 describes existing hand gesture recognition method used in our method. The proposed method will be presented in Section 4. Further, experimentation and newly developed gestures will be followed in Section 5. It includes hand gestures which use z- direction as well as those of x-y directions with one hand and two hands. The analyses and discussions are made in the same section. Finally, the conclusion and remarks are presented in Section Hand Area Detection in Near Distance Kinect v2 Sensor provides a depth sensor and an infrared sensor to provide depth information and infrared information. But in near distance less than 0.5 meter, it does not give the information. We propose the new method to detect hand area which can be used in the near distance. We used the characteristics of Kinect v2 sensor and infrared sensor which do not give the appropriate information including depth information and infrared radiation information in less than 0.5 meter. Specifically, we used the white areas of two resultant images caused by the side effect of sensor limitations. Figure 2 shows the images. Conducting AND operation of two images, for example (b) and (c), then we can extract the hand area. Another pair of (e) and (f) in Figure 2 can be used to extract hand area in the dark environment. (a) (b) (c) (d) (e) (f) Figure 2. Hand area detection by AND operation of depth image and infrared image. (a) Original Images. (b) Depth Images. (c) Infrared Images. (d) Original Image in darkness. (f) Depth Image in darkness. (g) Infrared Image in darkness Copyright c 2016 SERSC 409

4 Figure 3. The Process of Detecting Hand Region Figure 3 illustrates the process to detect hand region. After extracting the hand area, we conduct morphology opening operation to remove noises in the image. 3. Hand Gesture Recognition After we detected the hand area, we recognize specific gestures. We split the area by drawing a black circle in the center of a hand, then count the number of areas isolated. [6, 23] 410 Copyright c 2016 SERSC

5 3.1. Computation of Weight Centers for Hand Areas To detect the hand area, we should first obtain the center of a hand area, i.e., moment [24], and its coordinates described in [6, 23]. Then the center of a hand m is defined as pq, (1). p q m, x y f ( x, y) dxdy pq (1) And the detected hand area, 0 th -order moment, can be expressed as (2). m0,0 f ( x, y) dxdy (2) Furthermore, the centers of weight, first-order moment, using (3) and (4), respectively as follows: m 1,0 and m 0,1, can be obtained m1,0 dxdyxf ( x, y) (3) m0,1 dxdyyf ( x, y). (4) And, the coordinate x c, y c can be denoted by (5) and (6), respectively. x y c c m 1,0 (5) m m 0,0 0,1 (6) m 0, Division of Hand Areas Figure 4 shows the procedures described in Section 2. Figure 4 (a) shows the area detected, (b) shows the computed locations of hand areas, (c) shows the circles in the centers of hand areas and (d) shows the resulted areas divided by the black circles. Here, we count the number of areas divided and can recognize various gestures. Copyright c 2016 SERSC 411

6 (a) (b) (c) (d) Figure 4. Procedure to Recognize Hand Area Region. (a) Hand Area Obtained. (b) Computation of Centers of Hand Areas. (c) Drawing Virtual Circles. (d) Filling the Circles by Black Color 4. The Proposed Method We propose newly invented hand gestures by counting the number of contours (separate areas) which was isolated by a black circle in the weight center. After detecting the hand areas, we draw a rectangle which includes hand area and draw a black circle whose diameter is 1/3 of the height of the rectangle. Using the number of separate areas and the moving of the areas, we can invent various gestures Assumptions and Advantages The proposed method does not use depth information and infrared information directly, but uses the side effect (error images) outside the detection range of Microsoft Kinect v2 sensor. We assume that the hands are the nearest objects and are moving less than 0.5 meter from the sensor. We propose that this method can be used outside the detection range in the near distance; and simpler than existing method because it does not use complicated algorithms and show more stable detection performance and independent of color of hands, illumination status, and background objects unlike existing methods [1-3, 6, 10] Recognition of Hand Gestures We can discriminate various gestures by counting number of isolated areas. Figure 5 shows how to recognize the specific hand gestures. In Figure 4, (a) to (c) are for singlehand gesture, and (d) to (f) are for two-hand gestures. Here, (a) and (d) mean mouse release ; (b) and (e) mean mouse click ; (c) and (f) designate mode change. Further, (g) expresses zoom in an object but we can zoom out by moving the hands to the opposite direction. To change to the rotation mode, we close two fists like (f), and after that open fists like (e) and move two hands like (h). By counting the number of contours, we discriminate the gestures. For example, (a) has five contours, (b) has two contours, and (c) 412 Copyright c 2016 SERSC

7 has one contour respectively when one hand is used. And (d) has ten contours, (e) has four contours, and (f) has two contours respectively when two hands are used. (a) (b) (c) (d) (e) (f) (g) Figure 5. The Proposed Hand Gestures. (a) Mouse Release. (b) Mouse Click. (c). Mode Change. (d) Mouse Release. (e) Mouse Click. (f) Mode Change. (g) Zoom-in. (h) Rotation 4.3. New Gestures Which Use Z-Axis Information Since Kinect v2 sensor cannot provide depth information and infrared information in near distance, the depth is determined by the size of radius of a circle in the center of hand area. Here, the depth means the distance from the x-y plane of hand area toward the Kinect v2 sensor. Furthermore, we can call the direction of the distance z-axis because it is perpendicular to the x-y plane composed of x, y in (5) and (6). Figure 6 shows the four layers and detected hand areas. Figure 6 illustrates four layers which describe divided distances from hand area toward Kinect v2 sensor. Here, layer 1 to layer 4 are determined by the lengths of radii of the circles in the hand area. It should be noted that the left figures are drawn from the point of human, while right figures are drawn from the point of sensors. For example, the top left figure denotes the layer 1 which is the nearest from the hand area is chosen but the hand area of top right figure is the smallest in the point of sensor s view. Specifically, the thresholds of radii of circles are 38 pixels, 48 pixels, 58 pixels and 68 pixels for layer 1, layer 2, layer 3 and layer 4, respectively. (h) Copyright c 2016 SERSC 413

8 Figure 6. Z-axis values according to the depth which is the distance from x- y plane of the hand area toward the Kinect sensor: left figures are for human s view; right figures are for sensor s view. (a) Layer 1: The radius is larger than or equal to 38 pixels and less than 48 pixels. (b) Layer 2: The radius is larger than or equal to 48 pixels and less than 48 pixels. (c) Layer 3: The radius is larger than or equal to 58 pixels and less than 48 pixels. (d) Layer 4: The radius is larger than or equal to 68 pixels 4. Experimentation We used Kinect v2 sensor and conducted the experimentation in the distance less than 0.5 meter which the sensor does not provide depth information and infrared data. In addition to that, we used openframeworks Kinect v2 in order to make user interface which is based on C++ and opengl. Further, we used various libraries including the addons of the openframeworks, and cross-platforms. The addons include ofxopencv and ofxcv that enable opencv in openframeworks; and ofxkinect2 that is to use Kinect v2 sensor. For the operating system, Microsoft Windows 10 is used and Visual Studio 2015 community is installed. In Figure 7, (a) expresses a right posture to detect hand area for our method while (b) shows a bad posture which gives extra skin area from elbow to wrist incorrectly. In case of (b) the center of the hand area moves to the wrist part, so that the hand area is not extracted. Additionally, we have a palm of one hand tilted in various ways like (c), but have obtained valid results also. In (d), left figure means releasemouse, middle figure means hold the object by mouse-click, and right figure means move an object. When using one hand, we marked a dot on the selected object. Figures (e) and (f) are for the two-hand gestures. In the (e), the left figure denotes mouse release, the center figure denotes hold the object by mouse-click, and the right figure expresses zoom-out. Similarly, in figure (f), the left figure denotes mouse release, the middle figure denotes hold the object by mouse-click, and the right figure expresses rotation. The two dots around an object denote that the focus is on the object. 414 Copyright c 2016 SERSC

9 (a) (b) (c) (d) Copyright c 2016 SERSC 415

10 (e) (f) Figure 7. The Proposed Gestures. (a) A Correct Posture and the Detected Area. (b) An Incorrect Posture and Detected Area. (c) Tilted Palms and the Detected Areas. (e) Clicking an Object with Two Hands and Expanding an Image. (f) Clicking an Object with Two Hands and Rotation We have confirmed that our method is valid in various situations. When the subject person is changed, the hand area is changed. So that, the thresholds to determine the layers are changed according to the lengths of circles which are located at the center of hand areas. We experimented for three persons and confirmed that our method is stable for the ranges in Figure Conclusions This paper has proposed a new simple method to recognize gestures in near distance less than 0.5 meter where Kinect v2 sensor cannot provide depth information and infrared sensor data. The method tracks hand area and counts number of contours, and uses direction of contours. The proposed method is simpler than the existing method which detects finger tracking method because it only checks the number of areas divided by a black circle in the center of hand area and the moving direction. Further, it can be used to develop three-dimensional user interface, since it uses z-axis information using the length of radius of the circle located at the center of a hand area. The proposed hand gestures can be used instead of mouse clicking, dragging and moving, releasing a mouse, rotating an image with two hands, and scaling an image with two hands. The method expands the available ranges of Kinect v2 sensor and can be used also for Kinect v1 sensor. Acknowledgements We thank Hanbat National University. This research was supported by the research fund of Hanbat National University in This paper is a revised and expanded version of a paper entitled A Simple 3D Hand Gesture Interface Based on Hand Area Detection and Tracking" presented at MITA 2016 (The 12 th International Conference on Multimedia Information and Technology and Applications), Luang Prabang, Lao PDR, July 4-6, Copyright c 2016 SERSC

11 References [1] P. Premaratne, Human Computer Interaction Using Hand Gestures: Cognitive Science and Technology, Springer-Verlag New York Inc., (2014). [2] C.-H. Wu and W.-L. Chen and C. H. Lin, Depth-Based Hand Gesture Recognition, vol. 75, no. 12, (2016), pp [3] G. R. S. Murthy and R. S. Jadon, A Review of Vision Based Hand Gesture Recognition, International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, (2009), pp [4] A. Abgottspon, A Hand Gesture Interface for Investigating Real-Time Human-Computer Interaction, ECU098 Informatics, 300CDE Individual Project, Coventry Univ., UK, (2010). [5] A. M. Balazs, Hand and Finger Detection Using JavaCV, (2012). [6] H. Park, A Method for Controlling Mouse Movement Using a Real-Time Camera, Master s Thesis of Brown Univ., Providence, RI, USA, (2010). [7] J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman and A. Blake, Real- Time Human Pose Recognition in Parts from Single Depth Images, Communications of ACM, (2013), pp [8] (2016). [9] R. M. Gurav and P. K. Kadbe, Real Time Tracking and Contour Detection for Gesture Recognition Using OpenCV, 2015 International Conference on Industrial Instrumentation and Control (ICIC), (2015), pp [10] C. Zou, Y. Liu, J. Wang and H. Si, Deformable Part Model Based Hand Detection against Complex Backgrounds, Advances in Images and Graphics Technologies, Springer Link, vol. 634 of the series Comm. in Computer and Info. Science, (2016), pp [11] J. Molina and J. M. Martínez, A Synthetic Training Framework for Providing Gesture Scalability to 2.5D Pose-Based Hand Gesture Recognition Systems, Machine Vision and Applications, vol. 25, issue 5, (2014), pp [12] G. Goswami, M. Vatsa and R. Singh, Face Recognition with RGB-D Images Using Kinect, Face Recognition Across the Imaging Spectrum, Springer Link, (2016), pp [13] (2016). [14] (2016). [15] J. Valcik, J. Sedmidubsk and P. Zezula, Improving Kinect-Skeleton Estimation, Advanced Concepts for Intelligent Vision Systems, Springer Link, vol of Lecture Notes in Computer Science, (2015), pp [16] C. Kim, S. Yun, S.-W. Jung and C. S. Won, Color and Depth Image Correspondence for Kinect v2, Advanced Multimedia and Ubiquitous Engineering, Springer Link, vol. 354 of the series Lecture Notes in Electrical Engineering, (2015), pp [17] W. Song, A.V. Le, S. Yun, S.-W. Jung and C. S. Won, Depth Completion for Kinect v2 Sensor, Multimedia Tools and Applications, Springer Link, (2016), pp [18] L.-C. Chen, Y.-M. Cheng, P.-Y. Chu and F. E. Sandnes, The Common Characteristics of User-Defined and Mid-Air Gestures for Rotating 3D Digital Contents, Universal Access in Human-Computer Interaction Techniques and Environments, Springer Link, vol of the series Lecture Notes in Computer Science, (2016), pp [19] S. Samoil, and S. N. Yanushkevich, Depth Assisted Palm Region Extraction using the Kinect v2 Sensor, 2015 Sixth International Conference on Emerging Security Technologies, (2015), pp [20] H. Alabbasi, A. Gradinaru, F. Moldoveanu and A. Moldoveanu, Human Motion Tracking & Evaluation using Kinect v2 Sensor, The 5th IEEE International Conference on E-Health and Bioengineering, (2015). [21] Y. Lan, J. Li and Z. Ju, Data Fusion-based Real-Time Hand Gesture Recognition with Kinect v2, th International Conference on Human System Interactions (HSI), (2016). [22] M.-S. Kim and C. H. Lee, A Simple 3D Hand Gesture Interface Based on Hand Area Detection and Tracking, Proceedings of MITA 2016 (The 12 th International Conference on Multimedia Information and Technology and Applications), Luang Prabang, Lao PDR, (2016), pp [23] B. Ionescu, D. Coquin, P. Lambert and V. Buzuloiu, Dynamic Hand Gesture Recognition Using the Skeleton of the Hand, EURASIP Journal on Applied Signal Processing, vol. 13, (2005), pp [24] J. Kilian, Simple Image Analysis by Moments, (2001), pp Copyright c 2016 SERSC 417

12 Authors Min-Soo Kim, received his B.E. Degree in Computer and Information Engineering from Hanbat National University, Daejeon, Korea, in His current research interests include pattern recognition, digital image processing and human computer interfaces. Choong Ho Lee, received his B.E. and M.E. Degrees in Electronic Engineering from Yonsei University, Seoul, Korea, in 1985 and 1987, respectively. He also received his Ph.D. in Information Sciences from Tohoku University, Sendai, Japan in March of From 1985 to 2000, he was with KT as a researcher. Since 2000, he has been a professor in Graduate School of Information Communication Engineering of Hanbat National University. His current research interests include pattern recognition, digital image processing and mobile robot control. 418 Copyright c 2016 SERSC