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An ada boost based face detection system using parallel configurable architecture with optimized computation

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Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......

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An ada boost based face detection system using parallel configurable architecture with optimized computation

  1. 1. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com AN ADABOOST-BASED FACE DETECTION SYSTEM USING PARALLEL CONFIGURABLE ARCHITECTURE WITH OPTIMIZED COMPUTATION By A PROJECT REPORT Submitted to the Department of electronics &communication Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY In partial fulfillment of the requirements for the award of the degree Of MASTER OF TECHNOLOGY IN ELECTRONICS &COMMUNICATION ENGINEERING APRIL 2016
  2. 2. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CERTIFICATE Certified that this project report titled “An AdaBoost-Based Face Detection System Using Parallel Configurable Architecture With Optimized Computation” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. Signature of the Guide Signature of the H.O.D Name Name
  3. 3. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com DECLARATION I hereby declare that the project work entitled “An AdaBoost-Based Face Detection System Using Parallel Configurable Architecture With Optimized Computation” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate. (Student Name) (Reg.No) Place: Date:
  4. 4. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ACKNOWLEDGEMENT I am extremely glad to present my project “An AdaBoost-Based Face Detection System Using Parallel Configurable Architecture With Optimized Computation” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work. I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project. I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project. I wish to express my deep sense of gratitude to my guide Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project. I also express my sincere thanks to the all the staff members of Computer science for their kind advice. And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.
  5. 5. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ABSTRACT: With the development of image sensor technology, the AdaBoost-based face detections are widely used in many monitoring sensor networks and mobile-camera-based applications. Fast face detection with high detection accuracy and low power consumption in such kinds of applications is very important. Since the AdaBoost-based face detection exhibits characteristics of data computation in dual direction and data diversity, we propose an AdaBoost-based face detection system using parallel configurable architecture with optimized computation. The architecture consists of parallel configurable arrays and two-level shared memory systems. It achieves dual- direction-based integral image computation that improves parallel processing efficiency and enables the subwindow adaptive cascade classification for data diversity, which further improves the detection efficiency in diverse face detection. Compared with the state-of-the-art works, this work achieves maximal performance of 30 frames/s at 1080p detection speed and extreme low power consumption.
  6. 6. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com INTRODUCTION: Cyber, physical, and social computing (CPSCom) systems are integrating all kinds of everyday life devices into heterogeneous network environments in order to ubiquitously extend today’s various networks to monitor, interact, communicate, and control objects related to humans and thus intelligently serve human beings. CPSCom encompasses a holistic treatment of data, information, and knowledge from the cyber– physical–social worlds to integrate, correlate, interpret, and provide contextually relevant abstractions to humans. The CPSCom systems are viewed as the next phase of computing systems, building on current progress in cyber–physical systems, sociotechnical systems, and cyber–social systems to support the computing for human experience . However, one of the challenges the cyber, physical, and social computing facing is how to understand the data collected by all kinds of networks and make a decision to serve human better. This is an application level topic mainly about pattern recognition and data mining based on physical systems. One of the most important technologies is face detection, which is essential to identify people. It can be widely used in smart surveillance, human–computer interaction, and all kinds of consumer electronics. Since the AdaBoost algorithm exhibits high performance in the detection speed and accuracy in many practical applications, the AdaBoost-based face detection is one of the best face detection technology and has been widely used ,However, for the image sensor network or mobilecamera-based applications, the face detections with high processing speed and low power consumption, are required. In this work, we focus on these requirements and design higher efficient hardware architecture for AdaBoost-based face detection The original AdaBoost-based face detection algorithm,mainly includes integral and squared integral image computation and cascade classification. The integral image computation, including squared integral image computation, is both computation intensive and memory accessing intensive and exhibits the feature of data computation in dual direction. Dual direction here denotes the row and column directions that the accumulation operations are implemented. Improving the efficiency of integral image computation is very important to enhance the overall performance. Cascade classification is sensitive to the image contents. In different detection scenes, the detection exhibits data diversity in the image contents such as image size, distance of the face targets, and the number of face targets. The data diversity results in redundant data and brings useless computations caused by many false candidate face regions. The data diversity
  7. 7. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com requires the hardware to have the computation flexibility to reduce the useless computations according to the image contents. Current studies focusing on the integral image computation and cascade classification lack the considerations mentioned earlier. We divide the current implementations of AdaBoost-based face detection system into three categories, which are introduced in the following discussion. First, many works optimize the algorithm on general-purpose architectures. These general- purpose architectures have the computation flexibility because they are programmable. However, the sequential instruction execution and frequent memory accessing limit the efficiency of integral image computation. Since the algorithm is characterized by huge amounts of computation, exploiting the parallelism of the algorithm by using multicore- or multiprocessor-based hardware architectures, including the General Public Utilities, is an effective way to improve the processing capability. However, the power consumption of these works is large, which makes them lack the adaptability in diverse detection scenes. Second, in order to achieve real-time detections, some works implemented application-specified integrated circuits (ASICs), These works achieve fast computation on the integral image by ASIC implementations, but the fixed circuits make it lack the flexibility of cascade classification for data diversity. Kyrkou and Theocharides,proposed a flexible parallel architecture based on the systolic array to improve the processing efficiency of integral image computation. This architecture can be used in several different applications by training different classifiers; however, its integral image is computed only in one direction, and it lacks the design considerations for data diversity. Third, the flexible hardware such as a field-programmable gate array (FPGA) has also been used to process the detection algorithm , The FPGA can be reconfigured to accelerate the algorithm and adjust the parameters to achieve the computation flexibility on the detection accuracy and computation time. However, these FPGAs are fine-grained and statically reconfigurable, which bring the extra power consumption and limit the processing efficiency. The integral image computation is the key part in the algorithm and exhibits dual-direction computation; meanwhile, the cascade classification exhibits the flexibility requirements for data diversity. The contributions of our work can be concluded in three aspects. First, we propose the dual-direction-based integral image computation. It can improve the parallel processing efficiency of the integral image computation. Second, we propose the subwindow adaptive classification mechanism. Based on
  8. 8. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com history parameters of subwindows, we predict the new parameters for the current frame to reduce the redundant data and useless computations. Finally, we propose a parallel flexible architecture based on these methods. The experimental results show that this architecture can achieve high processing speed and low power consumption in the AdaBoost-based face detection. It also shows strong computation flexibility in diverse face detections.
  9. 9. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CONCLUSION: In this work, in order to achieve high processing speed and low power consumption in the AdaBoost-based face detection, we exploited the characteristics of data computation in the algorithm and proposed the methods of dual-direction-based integral image computation and subwindow adaptive classification for hardware design. A parallel flexible architecture based on these methods is proposed. Experimental results show that this architecture greatly improves the processing speed and reduces the power consumption for face detection. Since there are many other kinds of face detection algorithms to enhance the detection performance, the reconfigurable property of different face detection algorithms will be investigated, and the flexibility of architecture will be improved further.
  10. 10. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com REFERENCES: [1] A. Sheth, P. Anantharam, and C. Henson, “Physical–cyber–social computing: An early 21st century approach,” IEEE Intell. Syst., vol. 28, no. 1, pp. 78–82, Jan./Feb. 2013. [2] Y.-L. Tian et al., “IBM smart surveillance system (S3): Event based video surveillance system with an open and extensible framework,” Mach. Vis. Appl., vol. 19, no. 5/6, pp. 315–327, Oct. 2008. [3] S. P. Adhikari, H.-J. Yoo, and H. Kim, “Boosting-based on-road obstacle sensing using discriminative weak classifiers,” Sensors, vol. 11, no. 4, pp. 4372–4384, Apr. 2011. [4] D. Ghimire and J. Lee, “Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines,” Sensors, vol. 13, no. 6, pp. 7714–7734, Jun. 2013. [5] M. S. Bartlett, G. Littlewort, I. Fasel, and J. R. Movellan, “Real time face detection and facial expression recognition: Development and applications to human computer interaction” in Proc. CVPRW, 2003, vol. 5, p. 53. [6] K. H. An and M. J. Chung, “Cognitive face analysis system for future interactive TV,” IEEE Trans. Consum. Electron., vol. 55, no. 4, pp. 2271–2279, Nov. 2009. [7] M. Rahman and N. Kehtarnavaz, “Real-time face-priority auto focus for digital and cell-phone cameras,” IEEE Trans. Consum. Electron., vol. 54, no. 4, pp. 1506–1513, Nov. 2008. [8] S. Kim, J.-Y. Sim, and S. Yang, “Vision-based cleaning area control for cleaning robots,” IEEE Trans. Consum. Electron., vol. 58, no. 2, pp. 685–690, May 2012. [9] K. Kim, S. Lee, J.-Y. Kim, M. Kim, and H.-J. Yoo, “A configurable heterogeneous multicore architecture with cellular neural network for real-time object recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 11, pp. 1612–1622, Nov. 2009. [10] Y.-K. Chen, W. Li, and X. Tong, “Parallelization of AdaBoost algorithm on multi-core processors,” in Proc. IEEE Workshop SiPS, 2008, pp. 275–280.

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