Multi-View Face Detection Based on Multi-Features AdaBoost Collaborative Learning Algorithm

Abstract:

In View of the Multi-View Face Detection Problem under Complex Background, an Improved Face Detection Method Based on Multi-Features Boosting Collaborative Learning Algorithm Integrating Local Binary Pattern (LBP) is Presented. Firstly, Facial Skin Color Information is Used to Exclude most of the Background Regions. then, Haar-like Feature and LBP Feature are Extracted from Facial Candidate Regions and Inputted into a Modified Adaboost Algorithm to Obtain a Strong Classifier. Lastly, in Order to Improve the Detection Speed, Pyramid Classifier System Structure is Adopted to Determine the Face. the Experimental Results on CMU Standard Test Set and Life Photos, the Proposed Method has Achieved the Rapid Detection of Multi-View Face Image.

• Periodical: Advanced Materials Research (Volumes 998-999)
• Pages: 884-888
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.998-999.884
• Online since: July 2014
• Authors: Rong Bing Huang*, Hong Zhang, Chang Ming Shu
• Keywords: Adaboost, Face Detection, Haar-Like, Local Binary Pattern (LBP), Multi-View

References

[1] M. -H. Yang, D. Kriegman, andN. Ahuja, Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24(1)(2002), pp.34-58.

DOI: 10.1109/34.982883

[2] P. Viola, andM.J. Jones, Robust real-time face detection. International journal of computer vision, vol. 57(2)(2004), pp.137-154.

DOI: 10.1023/b:visi.0000013087.49260.fb

[3] P. Wang, andQ. Ji, Multi-view face and eye detection using discriminant features. Computer Vision and Image Understanding, vol. 105(2)(2007), pp.99-111.

DOI: 10.1016/j.cviu.2006.08.008

[4] Z. -B. Meng, H. Jiang, J. Cheng, et al., Feature pruning based AdaBoost and its application in face detection. Journal of Zhejiang University(Engineering Science), vol. 47(5)(2013), pp.906-911.

[5] Z. Zhang, L. Zhu, S.Z. Li, et al., Real-time multi-view face detection. Proc. of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, IEEE, vol. 2002), pp.142-147.

DOI: 10.1109/afgr.2002.1004147

[6] Q. -B. Li, J. -G. Liu, andZ. Huang, Multi-view face detection using skin color model and FloatBoost. Computer Engineering and Applications, vol. 49(23)(2013), pp.166-169.

[7] H. -Y. Chen, C. -L. Huang, andC. -M. Fu, Hybrid-boost learning for multi-pose face detection and facial expression recognition. Pattern Recognition, vol. 41(3)(2008), pp.1173-1185.

DOI: 10.1016/j.patcog.2007.08.010

[8] Z. -H. Zhou, Y. Jiang, Y. -B. Yang, et al., Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine, vol. 24(1)(2002), pp.25-36.

DOI: 10.1016/s0933-3657(01)00094-x

[9] T. Ahonen, A. Hadid, andM. Pietikainen, Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28(12)(2006), p.2037-(2041).

DOI: 10.1109/tpami.2006.244

[10] H. -J. Cheng, Multi-pose Face Detection and Recognition on Nonlinear Manifold. Hang Zhou: Zhejiang University, (2006).

[11] V. Jain, andA. Mukherjee, The Indian face database. http: /vis-www. cs. umass. edu/~vidit/ IndianFaceDatabase, (2002).

[12] P.J. Phillips, H. Moon, S.A. Rizvi, et al., The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22(10)(2000), pp.1090-1104.

DOI: 10.1109/34.879790

[13] H. Schneiderman, andT. Kanade, A statistical method for 3D object detection applied to faces and cars. IEEE Conference on Computer Vision and Pattern Recognition(CVPR2000) IEEE Press, vol. 1(2000), pp.746-751.

DOI: 10.1109/cvpr.2000.855895