Total views : 3982
A Hybrid Face Detection Approach in Color Images with Complex Background
Human face detection in colour images has been researched extensively over the past decades. Face detection has several applications in areas, such as security access control, visual surveillance, video conferencing, intelligent human-computer interfaces and content-based information retrieval. An ideal face detection system should detect faces from a given image/video regardless of their poses, illumination, scale, age, race, image quality, and image complexity with optimum speed and very low false-positive rate. In this paper a hybrid approach, based on the skin color information and Adaboost-based face detection, is proposed. The key points of the proposed framework are background elimination and down-sizing using the adaptive skin colour classification and segmentation, as well as sub-window size estimation. Therefore, the skin colour segments, as face candidates, were searched instead of the whole image. Meanwhile, the Viola-Jones Adaboost-based face detector was adopted in this research as the final face detector. In order to test the accuracy of the proposed algorithm, the proposed system was implemented and some experiments were also conducted on the standard image datasets such as Caltech (California Institute of Technology) standard image dataset. The proposed hybrid face detection system was compared with the Viola-Jones face detection system. The experiments showed that the proposed approach could efficiently improve the face detection system in both aspects, namely, accuracy (98.88%) and detection time (259.59 ms).
Color-Based, Face Detection, Illumination, Skin Color Classification.
- Nam M, Koh E, et al. An efficient face and eye detector modeling in external environment. Artificial Intelligence and Soft Computing. 2006; 4029:841-9.
- Jiang J, Ip HHS. A real-time hierarchical rule-based approach for scale independent human face detection. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series; 2007.
- Khanum A, Javed MY, Sohail S, Mufti M. A heuristically guided hybrid approach to face detection for content based image retrieval in internet images. 4th International Conference on Emerging Technologies; 2008. p. 237-41.
- Wang D, J Ren, et al. Skin detection from different color spaces for model-based face detection. Advanced Intelligent Computing Theories and Applications: With Aspects of Contemporary Intelligent Computing Techniques. 2008; 15:487-94.
- Lin HJ, Wang SY, Yen SH, Kao YT. Face detection based on skin color segmentation and neural network. International Conference on Neural Networks and Brain; 2005. p. 1144-9. ISBN: 0-7803-9422-4.
- Gizatdinova Y, Surakka V. Feature-based detection of facial landmarks from neutral and expressive facial images. IEEE Trans Pattern Anal Mach Intell. 2006 Jan; 28(1):135-9.
- Yan X, Xiao-Wei C. Multiple faces detection through facial features and modified Bayesian classifier. International Conference on Multimedia Information Networking and Security; 2009. p. 73-7.
- Akagunduz E, Ulusoy I. 3D face detection using transform invariant features. Electron Lett. 2010 Jun 24; 46(13):905-7.
- Liu Q, Peng G. A robust skin color based face detection algorithm, 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR); 2010. p. 525-8.
- Perez CA, Aravena CM, Vallejos JI, Estevez PA, Held CM. Face and iris localization using templates designed by particle swarm optimization. Pattern Recogn Lett. 2010; 31(9):857-68.
- Wang J.; Yang H. Face detection based on template matching and 2DPCA algorithm. Congress on Image and Signal Processing; 2008; p. 575-9.
- Chen W, Sun T, Yang X, Wang L. Face detection based on half face-template. 9th International Conference on Electronic Measurement & Instruments; 2009. p. 4-58.
- Li X, Li Y, Yao Y, Lv X, Chen J, Zhang L. Study and realization of face detection based on skin segmentation and template matching. 4th International Conference on New Trends in Information Science and Service Science (NISS); 2010. p. 375-8.
- Vadakkepat P, Lim P, et al. Multimodal approach to human-face detection and tracking. IEEE Trans Ind Electron. 2008; 55(3):1385-93.
- Ruan J, Yin J. Face detection based on facial features and linear support vector machines. IEEE International Conference on Communication Software and Networks; 2009. p. 371-5.
- Lang L, Gu W. A robustness and real-time face detection algorithm in complex background. Wavelet Analysis and Pattern Recognition; 2009; 22-5.
- Pavani SK, Delgado-Gomez D, Frangi AF. Haar-like features with optimally weighted rectangles for rapid object detection. Pattern Recogn. 2009; 43(1):160-72.
- Neoh HS, Hazanchuk A. Adaptive edge detection for real-time video processing using FPGAs. Global Signal Processing. 2004; 7(3):2-3.
- Tabatabaie ZS, Wirza R, Udzir NI, Kheirkhah E. Adaptive skin color classification technique for color-based face detection systems using integral image. IRECOS. 2011; 6(1):32-9.
- Haj M, Bagdanov, A, Gonzalez, J. Robust and efficient multipose face detection using skin color segmentation. Pattern Recogn Image Anal. 2009; 152-9.
- Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. 1986; 8(6):679-98.
- Hjelmas EB. Low. Face detection: a survey. Comput Vis Image Understand. 2001; 83(3):236-74.
- Viola P, M. Jones. Robust real-time face detection. Int J Comput Vis. 2004; 57(2):137-54.
- Fergus R. The Caltech face database. Caltech. Feb 2004. Available from: http://www.vision.caltech.edu/html-files/archive.html
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.