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




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, 9197-203 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, 49114-123 Google Scholar

[3] Rahman M, Kehtarnavaz N. Real-time face-priority auto focus for digital and cell-phone cameras. IEEE Trans Consum Electron, 2008, 541506-1513 CrossRef Google Scholar

[4] Xiong Y G, Pulli K. Color matching for high-quality panoramic images on mobile phones. IEEE Trans Consum Electron, 2010, 562592-2600 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, 601080-1089 CrossRef Google Scholar

[6] Yang M, Kriegman D, Ahuja N. Detecting faces in images: a survey. IEEE Trans Patt Anal Mach Intell, 2002, 2434-58 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, 2204-216 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, 5144-151 Google Scholar

[9] Viola P, Jones M. Robust real-time face detection. Int J Comput Vis, 2004, 57137-154 CrossRef Google Scholar

[10] Isobe T, Fujiwara M, Kaneta H. Development and features of a TV navigation system. IEEE Trans Consum Electron, 2003, 50393-399 Google Scholar

[11] Zuo F, de With P H N. Real-time embedded face recognition for smart home. IEEE Trans Consum Electron, 2005, 51183-190 CrossRef Google Scholar

[12] An K H, Chuang M J. Cognitive face analysis system for future interactive TV. IEEE Trans Consum Electron, 2009, 552271-2279 CrossRef Google Scholar

[13] Soowoong K, Jae-young S, Seungjoon Y. Vision-based cleaning area control for cleaning robots. IEEE Trans Consum Electron, 2002, 58685-690 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, 191034-1047 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, 1941-52 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, 57082406-52 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, 57082401-52 Google Scholar


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