Low-cost Natural Interface Based on Head Movements

Abstract Sometimes people look for freedom in the virtual world. However, not all have the possibility to interact with a computer in the same way. Nowadays, almost every job requires interaction with computerized systems, so people with physical impairments do not have the same freedom to control a mouse, a keyboard or a touchscreen. In the last years, some of the government programs to help people with reduced mobility suffered a lot with the global economic crisis and some of those programs were even cut down to reduce costs. This paper focuses on the development of a touchless human-computer interface, which allows anyone to control a computer without using a keyboard, mouse or touchscreen. By reusing Microsoft Kinect sensors from old videogames consoles, a cost-reduced, easy to use, and open-source interface was developed, allowing control of a computer using only the head, eyes or mouth movements, with the possibility of complementary sound commands. There are already available similar commercial solutions, but they are so expensive that their price tends to be a real obstacle in their purchase; on the other hand, free solutions usually do not offer the freedom that people with reduced mobility need. The present solution tries to address these drawbacks.

[1]  Zicheng Liu,et al.  Eye gaze tracking using an RGBD camera: a comparison with a RGB solution , 2014, UbiComp Adjunct.

[2]  Jian Sun,et al.  Face recognition with learning-based descriptor , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Aldric T. Negrier,et al.  PRHOLO Interactive Holographic Public Relations , 2015 .

[4]  Jin-Shin Lai,et al.  A novel position sensors-controlled computer mouse for the disabled , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[5]  Loris Nanni,et al.  Effective and precise face detection based on color and depth data , 2014 .

[6]  Jana Abhijit Kinect for Windows SDK Programming Guide , 2012 .

[7]  Hyun Seung Yang,et al.  Head pose estimation using image abstraction and local directional quaternary patterns for multiclass classification , 2014, Pattern Recognit. Lett..

[8]  Haibo Li,et al.  Direct three-dimensional head pose estimation from Kinect-type sensors , 2014 .

[9]  Ricardo Alves FATIMA REVISITED: AN INTERACTIVE INSTALLATION , 2014 .

[10]  Mads Torgersen,et al.  C# Programming Language , 2003 .

[11]  Al Stevens,et al.  C programming , 1990 .

[12]  Bogdan Kwolek,et al.  Using Kinect for Facial Expression Recognition under Varying Poses and Illumination , 2014, AMT.

[13]  Margrit Betke,et al.  Movement and Recovery Analysis of a Mouse-Replacement Interface for Users with Severe Disabilities , 2009, HCI.

[14]  S. Rajaee Web browser software with single switch interface for PDAs or computers , 2004, IEEE 30th Annual Northeast Bioengineering Conference, 2004. Proceedings of the.

[15]  Jian Sun,et al.  An associate-predict model for face recognition , 2011, CVPR 2011.

[16]  Lin Liang,et al.  AAM based face tracking with temporal matching and face segmentation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Min-Chun Hu,et al.  Eat as much as you can: a kinect-based facial rehabilitation game based on mouth and tongue movements , 2014, ACM Multimedia.