Abstract
This paper describes a face detection system based on the Blackfin microcomputer architecture that may be used in an Internet of Things (IoT) context. The face detection algorithm is based on skin detection and scanning binary images to determine the face area. Further image processing may determine the eyes and mouth in order to extract main face characteristics. The face detection algorithm may be used in context of IoT to determine the searching area for eyes and mouth (e.g. for face recognition and emotion detection). The face detection algorithm is implemented using the Visual DSP++ integrated development environment and face detection is achieved in real time.
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References
Lin, S.H., Kung, S.Y., Lin, L.J.: Face recognition/detection by probabilistic decision-based neural network. IEEE Trans. Neural Netw. 8(1), 114–132 (1997)
Chiang, C.-C., Tai, W.-K., Yang, M.-T., Huang, Y.-T., Huang, C.-J.: A novel method for detecting lips, eyes and faces in real time. Real-Time Imaging 9, 277–287 (2003)
Viola, P., Jones, M.J.: Real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Wang, Y.-Q.: An analysis of the viola-jones face detection algorithm. Image Process. Line 4, 129–148 (2014). https://doi.org/10.5201/ipol.2014.104
Kim, M.H., Joo, Y.H., Park, J.B.: Emotion detection algorithm using frontal face image. In: 2015 International Conference on Control Automation and Systems (ICCAS 2005), 2–5 June 2005, Kintex, Gyeong Gi, Korea, pp. 2373–2378 (2005)
Soriano, M., Huovinen, S., Martinkauppi, B., Laaksonen, M.: Using the skin locus to cope with changing illumination conditions in color-based face tracking. In: IEEE Nordic Signal Processing Symposium, Kolmarden, Suedia, pp. 383–386 (2000)
Gan, W.-S., Kuo, S.M.: Embedded Signal Processing with Micro Signal Architecture. Wiley-IEEE Press, Hoboken (2007)
Generalized Hough Transform. http://www.cs.cmu.edu/~16385/spring15/lectures/Lecture6.pdf
Analog Devices, Blackfin BF533 EZ-Kit Lite evaluation board. http://www.analog.com/en/design-center/evaluation-hardware-and-software/evaluation-boards-kits/BF533-EZLITE.html#eb-overview
ADSP-BF537 Blackfin® Processor Hardware Reference. http://www.analog.com/media/en/dsp-documentation/processor-manuals/ADSP-BF537_hwr_rev3.4.pdf
VisualDSP++ 5.0 User’s Guide, Revision 3.0, August 2007
Acknowledgments
This work has been partially funded by UEFISCDI Romania under Bridge Grant project grant no. 60BG/2016 “Intelligent communications system based on integrated infrastructure, with dynamic display and alerting - SICIAD. The authors would like to thank to Florin Rosulescu for his support to this work.
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Zoican, S., Vochin, M., Zoican, R., Galațchi, D. (2018). Face Detection in Internet of Things Using Blackfin Microcomputers Family. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 747. Springer, Cham. https://doi.org/10.1007/978-3-319-77700-9_13
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DOI: https://doi.org/10.1007/978-3-319-77700-9_13
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