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Real-Time Video Convolutional Face Finder on Embedded Platforms


A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF) algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.

[1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26]


  1. Yang M-H, Kriegman DJ, Ahuja N: Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(1):34-58. 10.1109/34.982883

    Article  Google Scholar 

  2. Yow KC, Cipolla R: Feature-based human face detection. Image and Vision Computing 1997,15(9):713-735. 10.1016/S0262-8856(97)00003-6

    Article  Google Scholar 

  3. Lin C-C, Lin W-C: Extracting facial features by an inhibitory mechanism based on gradient distributions. Pattern Recognition 1996,29(12):2079-2101. 10.1016/S0031-3203(96)00034-9

    Article  Google Scholar 

  4. Craw I, Tock D, Bennett A: Finding face features. Proceedings of the 2nd European Conference on Computer Vision (ECCV '92), May 1992, Santa Margherita Ligure, Italy 92-96.

    Google Scholar 

  5. Lanitis A, Taylor CJ, Cootes TF: Automatic face identification system using flexible appearance models. Image and Vision Computing 1995,13(5):393-401. 10.1016/0262-8856(95)99726-H

    Article  Google Scholar 

  6. Moghaddam B, Pentland A: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):696-710. 10.1109/34.598227

    Article  Google Scholar 

  7. Sung K-K, Poggio T: Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998,20(1):39-51. 10.1109/34.655648

    Article  Google Scholar 

  8. Garcia C, Delakis M: Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004,26(11):1408-1423. 10.1109/TPAMI.2004.97

    Article  Google Scholar 

  9. Tang X, Ou Z, Su T, Zhao P: Cascade AdaBoost classifiers with stage features optimization for cellular phone embedded face detection system. Proceedings of the 1st International Conference on Natural Computation (ICNC '05), August 2005, Changsha, China 688-697.

    Google Scholar 

  10. Viola P, Jones M: Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, December 2001, Kauai, Hawaii, USA 1: 511-518.

    Google Scholar 

  11. Kim J-B, Sung YH, Kee S-C: A fast and robust face detection based on module switching network. Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (FGR '04), May 2004, Seoul, Korea 409-414.

    Google Scholar 

  12. Theocharides T, Link G, Vijaykrishnan N, Irwin MJ, Wolf W: Embedded hardware face detection. Proceedings of the 17th IEEE International Conference on VLSI Design, January 2004, Mumbai, India 133-138.

    Chapter  Google Scholar 

  13. Rowley HA, Baluja S, Kanade T: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998,20(1):23-38. 10.1109/34.655647

    Article  Google Scholar 

  14. LeCun Y, Bottou L, Bengio Y, Haffner P: Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998,86(11):2278-2324. 10.1109/5.726791

    Article  Google Scholar 

  15. Tiwari V, Malik S, Wolfe A: Compilation techniques for low energy: an overview. Proceedings of IEEE Symposium on Low Power Electronics, October 1994, San Diego, Calif, USA 38-39.

    Google Scholar 

  16. Muchnick S: Advanced Compiler Design & Implementation. Morgan Kaufmann, San Francisco, Calif, USA; 1997.

    Google Scholar 

  17. Bauer JC, Closse E, Flamand E, Poize M, Pulou J, Penier P: SAXO: a retargetable optimized compiler for DSPs. Proceedings of the 8th International Conference on Signal Processing Applications & Technology (ICSPAT '97), September 1997, San Diego, Calif, USA

    Google Scholar 

  18. Palanciuc V, Badea D, Ilas C, Flamand E: A spill code reduction technique for EPIC architectures. Proceedings of the 1st Workshop on Explicitly Parallel Instruction Computing Architectures and Compiler Technology (EPIC-1 '01), September 2001, Austin, Tex, USA

    Google Scholar 

  19. INTEL PCA Optimization guide

  20. Van Achteren T, Deconinck G, Catthoor F, Lauwereins R: Data reuse exploration techniques for loop-dominated applications. Proceedings of Design, Automation and Test in Europe Conference and Exhibition (DATE '02), March 2002, Paris, France 428-435.

    Google Scholar 

  21. Catthoor F, Danckaert K, Kulkarni C, et al.: Data Access and Storage Management for Embedded Programmable Processors. Kluwer Academic, Boston, Mass, USA; 2002.

    Book  MATH  Google Scholar 

  22. Simunic T, Benini L, De Micheli G, Hans M: Source code optimization and profiling of energy consumption in embedded systems. Proceedings of the 13th International Symposium on System Synthesis (ISSS '00), September 2000, Madrid, Spain 193-198.

    Chapter  Google Scholar 

  23. Bateman A, Paterson-Stephens I: The DSP Handbook, Algorithms, Applications and Design Techniques. Prentice-Hall, Upper Saddle River, NJ, USA; 2002.

    Google Scholar 

  24. SC140 DSP Core Reference Manual Second Revision Motorola Corporation, 2001

  25. MPEG-4 visual version 1 : Coding of audio-visual objects—Part 2: visual. ISO/IEC JTC1 14 496-2, 1999

  26. Duffner S, Garcia C: A connexionist approach for robust and precise facial feature detection in complex scenes. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis (ISPA '05), September 2005, Zagreb, Croatia 316-321.

    Google Scholar 

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Correspondence to Franck Mamalet.

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Mamalet, F., Roux, S. & Garcia, C. Real-Time Video Convolutional Face Finder on Embedded Platforms. J Embedded Systems 2007, 021724 (2007).

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  • Feature Extraction
  • False Alarm Rate
  • Face Detection
  • Memory Allocation
  • Code Size