Kinect Who's Coming—Applying Kinect to Human Body Height Measurement to Improve Character Recognition Performance

A great deal of relevant research on character recognition has been carried out, but a certain amount of time is needed to compare faces from a large database. The Kinect is able to obtain three-dimensional coordinates for an object (x & y axes and depth), and in recent years research on its applications has expanded from use in gaming to that of image measurement. This study uses Kinect skeleton information to conduct body height measurements with the aim of improving character recognition performance. Time spent searching and comparing characters is reduced by creating height categories. The margin of error for height used in this investigation was ± 5 cm; therefore, face comparisons were only executed for people in the database within ±5 cm of the body height measured, reducing the search time needed. In addition, using height and facial features simultaneously to conduct character recognition can also reduce the frequency of mistaken recognition. The Kinect was placed on a rotary stage and the position of the head on the body frame was used to conduct body tracking. Body tracking can be used to reduce image distortion caused by the lens of the Kinect. EmguCV was used for image processing and character recognition. The methods proposed in this study can be used in public safety, student attendance registration, commercial VIP recognition and many others.

[1]  Hau-Wei Lee,et al.  Vision Servo Motion Control and Error Analysis of a Coplanar Stage for Image Alignment Motion , 2013 .

[2]  Kevin Hapeshi,et al.  A new method for face detection in colour images for emotional bio-robots , 2010 .

[3]  Anil K. Jain,et al.  Matching Composite Sketches to Face Photos: A Component-Based Approach , 2013, IEEE Transactions on Information Forensics and Security.

[4]  Sander Oude Elberink,et al.  Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications , 2012, Sensors.

[5]  Andrea Sanna,et al.  A kinect-based interface to animate virtual characters , 2012, Journal on Multimodal User Interfaces.

[6]  Jenq-Shyong Chen,et al.  Development of a High Precision Edge Alignment System for Touch-Panel Glass Substrates , 2014 .

[7]  Andrew W. Fitzgibbon,et al.  KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera , 2011, UIST.

[8]  Yu-Ching Lin,et al.  The Detection Techniques for Several Different Types of Fiducial Markers , 2013 .

[9]  Alice Caplier,et al.  Enhanced Patterns of Oriented Edge Magnitudes for Face Recognition and Image Matching , 2012, IEEE Transactions on Image Processing.

[10]  Ling Shao,et al.  Enhanced Computer Vision With Microsoft Kinect Sensor: A Review , 2013, IEEE Transactions on Cybernetics.

[11]  Yuki Yoshida,et al.  HUMAN GAIT BEHAVIOR INTERPRETATION BY A MOBILE HOME HEALTHCARE ROBOT , 2012 .

[12]  Anthony J. T. Lee,et al.  A data mining approach to face detection , 2010, Pattern Recognit..

[13]  Luis Salgado,et al.  Depth-Color Fusion Strategy for 3-D Scene Modeling With Kinect , 2013, IEEE Transactions on Cybernetics.