SCIENCE CHINA Information Sciences, Volume 59, Issue 12: 122402(2016) https://doi.org/10.1007/s11432-015-5312-z

A fast face detection architecture for auto-focus in smart-phones and digital cameras

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  • ReceivedOct 9, 2015
  • AcceptedDec 2, 2015
  • PublishedMar 17, 2016


Auto-focus is very important for capturing sharp human face centered images in digital and smart phone cameras. With the development of image sensor technology, these cameras support more and more high-resolution images to be processed. Currently it is difficult to support fast auto-focus at low power consumption on high-resolution images. This work proposes an efficient architecture for an AdaBoost-based face-priority auto-focus. The architecture supports block-based integral image computation to improve the processing speed on high-resolution images; meanwhile, it is reconfigurable so that it enables the sub-window adaptive cascade classification, which greatly improves the processing speed and reduces power consumption. Experimental results show that 96\% detection rate in average and 58 fps (frame per second) detection speed are achieved for the 1080p (1920$\times$1080) images. Compared with the state-of-the-art work, the detection speed is greatly improved and power consumption is largely reduced.



This work was supported in part by China Major Science and Technology (S&T) Project (Grant No. 2013ZX01033-001-001-003), National High-Tech R&D Program of China (863) (Grant Nos. 2012AA01-2701, 2012AA0109-04), National Natural Science Foundation of China (Grant No. 61274131), International S&T Cooperation Project of China (Grant No. 2012DFA11170), and Importation and Development of the High-Caliber Talents Project of Beijing Municipal Institutions (Grant No. YETP0163).


[1] Kehtarnavaz N, Oh H. Development and real-time implementation of a rule-based auto-focus algorithm. Real-Time Imag, 2003, 9: 197-203 CrossRef Google Scholar

[2] Peddigari V, Gamadia M, Kehtarnavaz N. Real-time implementation issues in passive automatic focusing for digital still camera. J Imag Sci Technol, 2005, 49: 114-123 Google Scholar

[3] Rahman M, Kehtarnavaz N. Real-time face-priority auto focus for digital and cell-phone cameras. IEEE Trans Consum Electron, 2008, 54: 1506-1513 CrossRef Google Scholar

[4] Xiong Y G, Pulli K. Color matching for high-quality panoramic images on mobile phones. IEEE Trans Consum Electron, 2010, 56: 2592-2600 CrossRef Google Scholar

[5] Chandrasekaran V, Dantu R, Jonnada S, et al. Cuffless differential blood Pressure estimation using smart phones. IEEE Trans Biomed Eng, 2013, 60: 1080-1089 CrossRef Google Scholar

[6] Yang M, Kriegman D, Ahuja N. Detecting faces in images: a survey. IEEE Trans Patt Anal Mach Intell, 2002, 24: 34-58 CrossRef Google Scholar

[7] Huang D Y, Lin C J, Hu W C. Learning-based face detection by adaptive switching of skin color models and AdaBoost under varying illumination. J Inf Hid Multimed Signal Process, 2011, 2: 204-216 Google Scholar

[8] Zhang Z W, Wang M H, Lu Z M, et al. A skin color model based on modified GLHS space for face detection. J Inf Hid Multimed Signal Process, 2014, 5: 144-151 Google Scholar

[9] Viola P, Jones M. Robust real-time face detection. Int J Comput Vis, 2004, 57: 137-154 CrossRef Google Scholar

[10] Isobe T, Fujiwara M, Kaneta H. Development and features of a TV navigation system. IEEE Trans Consum Electron, 2003, 50: 393-399 Google Scholar

[11] Zuo F, de With P H N. Real-time embedded face recognition for smart home. IEEE Trans Consum Electron, 2005, 51: 183-190 CrossRef Google Scholar

[12] An K H, Chuang M J. Cognitive face analysis system for future interactive TV. IEEE Trans Consum Electron, 2009, 55: 2271-2279 CrossRef Google Scholar

[13] Soowoong K, Jae-young S, Seungjoon Y. Vision-based cleaning area control for cleaning robots. IEEE Trans Consum Electron, 2002, 58: 685-690 Google Scholar

[14] Hanai Y, Hori Y, Nishimura J, et al. A versatile recognition processor employing Haar-like feature and cascade classifier. In: Proceedings of IEEE International Conference on Solid-State Circuits, San Francisco, 2009. 148--149. Google Scholar

[15] Kyrkou C, Theocharides T. A flexible parallel hardware architecture for AdaBoost-based real-time object detection. IEEE Trans Very Large Scale Integr Syst, 2011, 19: 1034-1047 CrossRef Google Scholar

[16] Hiromoto M, Sugano H, Miyamoto R. Partially parallel architecture for AdaBoost-based detection with Haar-like features. IEEE Trans Circ Syst Vid Technol, 2009, 19: 41-52 CrossRef Google Scholar

[17] Liu L B, Chen Y J, Wang D, et al. Implementation of multi-standard video decoder on a heterogeneous coarse-grained reconfigurable processor. Sci China Inf Sci, 2014, 57: 082406-52 Google Scholar

[18] Liu L B, Chen Y J, Yin S Y, et al. Implementation of AVS Jizhun decoder with HW/SW partitioning on a coarse-grained reconfigurable multimedia system. Sci China Inf Sci, 2014, 57: 082401-52 Google Scholar

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