In-Vehicle Real-Time Human Emotion Recognition Through Visual, Thermal And Heart Rate Monitoring Using Parallel CPU And GPU Techniques For Driver Safety

Goh Chia Chieh*, Dino Isa**
* Researcher university of nothing Malaysia Campus Department Electrical & Electronics Engineering
** Professor University of Nottingham Malaysia Campus Department Electrical & Electronics Engineering
Periodicity:June - August'2011
DOI : https://doi.org/10.26634/jele.1.4.1510

Abstract

Real-Time Human Emotion Recognition Is A Challenging Task. Speed And Accuracy Has Always Been The Main Concern For Human Emotion Analysis. Such System Poses An Even Greater Challenge For Implementation In A Limited Space Of A Constantly Mobile Environment, For Example, In A Moving Vehicle. On Top Of That, Power Consumption Is Another Major Issue To Be Considered. This Paper Proposes An Efficient Method On How Such System Can Be Implemented In The Mobile Environment By Utilizing Parallel Cpu And Gpu To Achieve Real-Time Emotion Analysis And Judgment. Using Low Power Consumption Single Board Computer (Sbc) With A Dual Graphic Card Installed, The Task Will Be Split Into Multiple Threads Which Process Visual, Thermal And Heart Rate Emotion Monitoring System Simultaneously. Thus, Better Accuracy And Optimal Performance Is Attained Through Parallel Gpu And Cpu Processing. Handling Enormous Of Data Through Parallel Processing Requires Expertise In Combining The Cpu And Gpu Tasks Together By Merging Different Kinds Of Data Competently.

Keywords

Real-Time Emotion Recognition, Parallel Computing, CUDA, Thermal Imaging, Heart Rate Monitoring.

How to Cite this Article?

Goh Chia Chieh and Dino Isa (2011). In-Vehicle Real-Time Human Emotion Recognition Through Visual, Thermal and Heart Rate Monitoring Using Parallel CPU and GPU Techniques for Driver Safety. i-manager’s Journal on Electronics Engineering, 1(4), 43-49. https://doi.org/10.26634/jele.1.4.1510

References

[ 1 ] . Paul Ekman.(1972).Retrieved from http://face.paulekman.com/default.aspx?override=1
[2]. Active Shape Model. (1995). Retrieved from http://webdocs.cs.ualberta.ca/~nray1/CMPUT615/Snake /cootes_cviu95.pdf
[3]. ARM. (2011). Retrieved from http://www.arm.com/
[ 4 ] .Open MP(2011).Retrieved from http://openmp.org/wp/
[5]. Andrea Selinger, D. A. (2004). Face Recognition in the Dark. 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 8 , (p. 129).
[6]. ANT+. (2010). Retrieved from Garmin ANT+: http://thisisant.com/
[7]. Blackburn, Dr. Gordon. (2010). Retrieved from clevelandclinic: http://my.clevelandclinic.org/heart/ prevention/exercise/pulsethr.aspx
[8]. Buddharaju, P. D. (2005). Automatic Thermal Monitoring System (ATHEMOS) for Deception Detection. Computer Vision and Pattern Recognition, 2005. CVPR 2005. (p. 1179). IEEE Computer Society Conference.
[9]. Buddharaju, P., Pavlidis, I., & Tsiamyrtzis, P. ( 2006 ). Pose-Invariant Physiological Face Recognition in the Thermal Infrared Spectrum. Computer Vision and Pattern Recognition Workshop, (p. 53).
[10] .CULA.(2011).CULA.Retrieved from http://www.culatools.com/
[11].dmihailescu.(201 0).Retrieved from http://www.codeproject.com/KB/dotnet/RuntimePerform ance.aspx
[12] . FERET(2000).FERET.Retrieved from http://www.itl.nist.gov/iad/humanid/feret/feret_master.ht ml
[13]. FFMEPG. (2011). FFMEPG. Retrieved from http://www.ffmpeg.org/
[14]. GTK. (2011). GTK. Retrieved from http://www.gtk.org/
[ 1 5 ] .Figure 2.(Image). Retrieved from http://face.paulekman.com/default.aspx
[ 1 6 ]. Figure 4.(Image).Retrieved from http://www.heartmath.org
[17]. Li, S. Z.-F.-C. (April 2007). Illumination Invariant Face Recognition Using Near-Infrared Images. Pattern Analysis and Machine Intelligence, (pp. 627 - 639 ). IEEE Transactions on Volume 29, Issue 4,.
[18]. Microsoft .net. (2011). Microsoft .net. Retrieved from http://www.microsoft.com/net/
[19]. Microsoft DirectShow. (2011). Microsoft DirectShow. Retrieved from http://msdn microsoft.com/en - us/library/dd375454(v=vs.85).aspx
[20]. Microsoft Visual Studio. (2011). Microsoft Visual Studio. Retrieved from http://www.microsoft.com/ visualstudio/en-us
[21]. NVidia CUDA. (2011). NVidia CUDA. Retrieved from http://www.nvidia.com/object/cuda_home_new.html
[22]. NVidia CUDA Programming Guide. (2011). NVidia CUDA Programming Guide. Retrieved from http://developer.download.nvidia.com/compute/cuda/ 1_0/NVIDIA_CUDA_Programming_Guide_1.0.pdf
[23]. OpenCV. (2011). OpenCV. Retrieved from http://opencv.willowgarage.com/wiki/
[24]. Socolinsky, D. A. (June 2006). Image Intensification for Low-Light Face Recognition. Computer Vision and Pattern Recognition Workshop, (p. 41 ).
[25]. Windows Media Foundation. (2010). Windows Media Foundation. Retrieved from http://msdn.microsoft.com/en-us/librar y/ms694197 (v=vs.85).aspx
[26]. WxWidgets. (2011). WxWidgets. Retrieved from http://www.wxwidgets.org/
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.