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http://dx.doi.org/10.14372/IEMEK.2013.8.3.137

Implementation of Face Detection System on Android Platform for Real-Time Applications  

Han, Byung-Gil (ETRI)
Lim, Kil-Taek (ETRI)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.8, no.3, 2013 , pp. 137-143 More about this Journal
Abstract
This paper describes an implementation of face detection technology for a real-time application on the Android platform. Java class of Face-Detection for detection of human face is provided by the Android API. However, this function is not suitable to apply for the real-time applications due to inadequate detection speed and accuracy. In this paper, the AdaBoost based classification method which utilizes Local Binary Pattern (LBP) histogram is employed for face detection. The face detection module has been developed by C/C++ language for high-speed image processing, and this module is included to the Android platform using the Java Native Interface (JNI). The experiments were carried out in the Java-based environment and JNI-based environment. The experimental results have shown that the performance of JNI-based is faster than Java-based method and our system is well enough to apply for real-time applications.
Keywords
Face detection; Android platform; JNI; LBP; AdaBoost;
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1 J. Ren, X. Jiang, J. Yuan, "A complete and fully automated face verification system on mobile devices," Pattern Recognition, Vol. 46, No. 1, pp.45-56, 2013.   DOI   ScienceOn
2 A. Pabbaraju, S. Puchakayala, "Face Recognition in Mobile Devices," Electrical Engineering and Computer Science, University of Michigan, 2010.
3 K. Lu, L. Dong, "Using LBP histogram for face recognition on Android platform," Proceedings on International Conference of Computer Research and Development, Vol. 1, pp.266-268, 2011.
4 M. Turk, A. Pentland, "Face Recognition Using Eigenfaces," Proceedings on IEEE Conference of Computer Vision and Pattern Recognition, pp.586-591, 1991.
5 H. Rowley, S. Baluja, T. Kanade, "Neural Network-Based Face Detection," Proceedings on IEEE Conference of Pattern Recognition and Machine Intelligence, pp.1-27, 1998.
6 P. Viola, M. Jones, "Robust Real-Time Face Detection", International Journal of Computer Vision, Vol. 57, No. 2, pp.137-154. 2004.   DOI   ScienceOn
7 D. Le, S. Satoh, "A multi-stage approach to fast face detection," IEICE Trans. Inf. & Syst. Vol. E89-D, No. 7, pp.2275-2285, 2006.   DOI
8 C. Kublebeck, A. Ernst, "Face detection and tracking in video sequences using the modified census transformation," Image and Vision Computing Vol. 24, No. 6, pp. 564-572, 2006.   DOI   ScienceOn
9 R. Feris, Y. Tian, A. Hampapur, "Capturing People in Surveillance Video," Proceedings on IEEE Conference of Computer Vision and Pattern Recognition, pp.1-8, 2007.
10 T. Ahonen, A. Hadid, M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, pp.2037-2041, 2006.   DOI   ScienceOn
11 OpenCV Library, http://opencv.org