A High Performance Dual Stage Face Detection Algorithm Implementation using FPGA Chip and DSP Processor
الموضوعات :
M V Ganeswara Rao
1
,
P Ravi Kumar
2
,
T Balaji
3
1 - Department of Electronics and Communication Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, AP, India.
2 - Department of Electronics and Communication Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, AP, India.
3 - Department of Electronics and Communication Engineering PVP Siddhartha Institute of Technology, Vijayawada, AP, India
الکلمات المفتاحية: Face detection, Heterogeneous System, FPGA, DSP,
ملخص المقالة :
A dual stage system architecture for face detection based on skin tone detection and Viola and Jones face detection structure is presented in this paper. The proposed architecture able to track down human faces in the image with high accuracy within time constrain. A non-linear transformation technique is introduced in the first stage to reduce the false alarms in second stage. Moreover, in the second stage pipe line technique is used to improve overall throughput of the system. The proposed system design is based on Xil inx’s Virtex FPGA chip and Texas Instruments DSP processor. The dual port BRAM memory in FPGA chip and EMIF (External Memory Interface) of DSP processor are used as interface between FPGA and DSP processor. The proposed system exploits advantages of both the computational elements (FPGA and DSP) and the system level pipelining to achieve real time perform ance. The present system implementation focuses on high accurate and high speed face detec tion and this system evaluated using standard BAO image database, which include images with different poses, orientations, occlusions and illumination. The proposed system attained 16.53 FPS frame rate for the input image spatial resolution of 640X480, which is 23.4 times faster detection of faces compared to MATLAB implementation and 12.14 times faster than DSP implementation and 2.1 times faster than FPGA implementation.
[1] Y. Lei, Z. Gang, R. Si-Heon, Lee Choon-Young, Lee Sang-Ryong and K. -M. Bae, "The Platform of Image Acquisition and Processing System Based on DSP and FPGA," 2008 International Conference on Smart Manufacturing Application, 2008, pp. 470-473, doi: 10.1109/ICSMA.2008.4505567.
[2] C. Kotropoulos and I. Pitas, "Rule-based face detection in frontal views," 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997, pp. 2537-2540 vol.4, doi: 10.1109/ICASSP.1997.595305.
[3] D. Nguyen, D. Halupka, P. Aarabi and A. Sheikholeslami, "Real-time face detection and lip feature extraction using field-programmable gate arrays," in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 4, pp. 902-912, Aug. 2006, doi: 10.1109/TSMCB.2005.862728.
[4] D. N. Arya, K. L. V. Sivanji, R. Reddy, S. Sivanantham, and K. Sivasankaran, “A face detection system implemented on FPGA based on RCT colour segmentation,” Proc. 2016 Online Int. Conf. Green Eng. Technol. IC-GET 2016, 2017, doi: 10.1109/GET.2016.7916781.
[5] H. Ben Fekih, A. E. B, and B. Juurlink, “An Efficient and Flexible FPGA Implementation of a Face Detection System,” pp. 243–254, 2015, doi: 10.1007/978-3-319-16214-0.
[6] H.-Y. Leung, L.-M. Cheng, and X. Y. Li, “A FPGA implementation of facial feature extraction,” J. Real-Time Image Process., vol. 10, no. 1, pp. 135–149, 2015, doi: 10.1007/s11554-012-0263-8.
[7] A. S. Kamewar, "Processing geospatial images using GPU," 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI), 2017, pp. 27-32, doi: 10.1109/ETIICT.2017.7977005.
[8] J. Batlle, “A New FPGA/DSP-Based Parallel Architecture for Real-Time Image Processing,” Real-Time Imaging, vol. 8, no. 5, pp. 345–356, 2002, doi: 10.1006/rtim.2001.0273.
[9] K. L. Y. Li et al., “A new parallel particle filter face tracking method based on heterogeneous system,” J. Real-Time Image Process., vol. 7, no. 3, pp. 153–163, 2012, doi: 10.1007/s11554-011-0225-6.
[10] L. Guo, “An embedded multimedia communication terminal based on DSP+FPGA,” Multimed. Tools Appl., vol. 76, no. 16, pp. 16949–16961, 2017, doi: 10.1007/s11042-016-3597-6.
[11] Z. Ding, F. Zhao, T. Wang, W. Shu, and M.-Y. Wu, “Hecto-Scale Frame Rate Face Detection System for SVGA Source on FPGA Board,” 2011 IEEE 19th Annu. Int. Symp. Field-Programmable Cust. Comput. Mach., pp. 37–40, 2011, doi: 10.1109/FCCM.2011.16.
[12] Rein-Lien Hsu, M. Abdel-Mottaleb and A. K. Jain, "Face detection in color images," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696-706, May 2002, doi: 10.1109/34.1000242.
[13] P. Viola and M. Jones, “Robust real-time face detection,” Int. J. Comput. Vis., vol. 57, no. 2, pp. 137–154, 2004, doi: 10.1023/B:VISI.0000013087.49260.fb. [14] F. Zhao, L. Yang, Y. Zhu, and P. Liao, “Ehancing the implementation of Adaboost algorithm on a DSP-based platform,” Int. Conf. Scalable Comput. Commun. - 8th Int. Conf. Embed. Comput. ScalCom-EmbeddedCom 2009, pp. 393–395, 2009, doi: 10.1109/EmbeddedCom-ScalCom.2009.77.
[15] Ganeswara Rao M.V., Panakala R.K., Mallikarjuna Prasad A. (2018) A New VLSI Architecture for Skin Tone Detection in an Uncontrolled Background. In: Anguera J., Satapathy S., Bhateja V., Sunitha K. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 471. Springer, Singapore.
[16] Fekih H.B., Elhossini A., Juurlink B. (2015) An Efficient and Flexible FPGA Implementation of a Face Detection System. In: Sano K., Soudris D., Hübner M., Diniz P. (eds) Applied Reconfigurable Computing. ARC 2015. Lecture Notes in Computer Science, vol 9040. Springer, Cham.
[17] Dong Zhang, S. Z. Li and D. Gatica-Perez, "Real-time face detection using boosting in hierarchical feature spaces," Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., Cambridge, 2004, pp. 411-414 Vol.2.
[18] Y. N. Chae, T. Han, Y.-H. Seo, and H. S. Yang, “An efficient face detection based on color-filtering and its application to smart devices,” Multimed. Tools Appl., vol. 75, no. 9, pp. 4867–4886, 2016, doi: 10.1007/s11042-013-1786-0.
[19] C. Kumar and M. S. Azam, “A multi-processing architecture for accelerating Haar-based face detection on FPGA,” 9th Int. Conf. Ind. Inf. Syst. ICIIS 2014, 2015, doi: 10.1109/ICIINFS.2014.7036525.
[20] S. Liao, A. K. Jain, and S. Z. Li, “A Fast and Accurate Unconstrained Face Detector,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp. 211–223, 2016, doi: 10.1109/TPAMI.2015.2448075.
[21] A. N. Rajagopalan, K. S. Kumar, J. Karlekar, R. M. M. M. Patil, U. B. Desai, and P. G. P. S. Chaudhuri, “Finding Faces in Photographs,” IEEE Int. Conf. Comput. Vis., no. 1, pp. 640–645, 1998, doi: 10.1109/ICCV.1998.710785.
[22] M. S. Lew, "Information theoretic view-based and modular face detection," Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, Killington, VT, USA, 1996, pp. 198-203.
[23] A. J. Colmenarez and T. S. Huang, “Face Detection With Informat ion- Based Maximum Discrimination,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 782–787, 1997, doi: http://dx.doi.org/10.1109/CVPR.1997.609415.
[24] K. S. Park, R. H. Park, and Y. G. Kim, “Face detection using the 3x3 block rank patterns of gradient magnitude images and a geometrical face model,” Dig. Tech. Pap. - IEEE Int. Conf. Consum. Electron., no. c, pp. 793–794, 2011, doi: 10.1109/ICCE.2011.5722867.
[25] P. P. Paul and M. Gavrilova, “PCA based geometric modeling for automatic face detection,” Proc. - 2011 Int. Conf. Comput. Sci. Its Appl. ICCSA 2011, pp. 33–38, 2011.
[26] A. Majumder, L. Behera, and V. K. Subramanian, “Automatic and robust detection of facial features in frontal face images,” Proc. - 2011 UKSim 13th Int. Conf. Model. Simulation, UKSim 2011, pp. 331–336, 2011, doi: 10.1109/UKSIM.2011.69.
[27] J. Guo, C. Lin, M. Wu, C. Chang and H. Lee, "Complexity Reduced Face Detection Using Probability-Based Face Mask Prefiltering and Pixel-Based Hierarchical-Feature Adaboosting," in IEEE Signal Processing Letters, vol. 18, no. 8, pp. 447-450, Aug. 2011.
[28] Katkoori Arun Kumar and Ravi Boda, “A Threshold-based Brain Tumour Segmentation from MR Images using Multi-Objective Particle Swarm Optimization,” Journal of Information Systems and Telecommunication, Vol. 9, No. 4, 2021, pp. 218–225.
[29] Hamed Agahi and Kimia Rezaei, “An Automatic Thresholding Approach to Gravitation-Based Edge Detection in Grey-Scale Images,” Journal of Information Systems and Telecommunication, Vol. 9, No. 4, 2021, pp. 285–296.
[30] K. Li, Y. Tian, B. Wang, Z. Qi, and Q. Wang, "Bi-Directional Pyramid Network for Edge Detection," Electronics, vol. 10, no. 3, 2021, p. 329-333.
[31] D. Wang, J. Yin, C. Tang, X. Cheng, and B. Ge, "Color edge detection using the normalization anisotropic Gaussian kernel and multichannel fusion," IEEE Access, vol. 8, 2020, pp. 228277-228288,.
[32] Azamossadat Nourbakhsh, Mohammad-Shahram Moin and Arash Sharifi, “Facial Images Quality Assessment based on ISO/ICAO Standard Compliance Estimation by HMAX Model,” Journal of Information Systems and Telecommunication, Vol. 7, No. 27, 2009, pp. 225–237.
[33] Azar Mahmoodzadeh, “Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features,” Journal of Information Systems and Telecommunication, Vol. 9, No. 36, 2021, pp. 45–52.
