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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 1, Pages 108-123, DOI: https://doi.org/10.21307/ijssis-2017-862
License : (CC BY-NC-ND 4.0)
Received Date : 03-November-2015 / Accepted: 12-January-2016 / Published Online: 27-December-2017
Human face detection is an indispensable component in face processing applications, including automatic face recognition, security surveillance, facial expression recognition, and the like. This paper presents a profile face detection algorithm based on curvelet features, as curvelet transform offers good directional representation and can capture edge information in human face from different angles. First, a simple skin color segmentation scheme based on HSV (Hue – Saturation - Value) and YCgCr (luminance - green chrominance - red chrominance) color models is used to extract skin blocks. The segmentation scheme utilizes only the S and CgCr components, and is therefore luminance independent. Features extracted from three frequency bands from curvelet decomposition are used to detect face in each block. A support vector machine (SVM) classifier is trained for the classification task. In the performance test, the results showed that the proposed algorithm can detect profile faces in color images with good detection rate and low misdetection rate.
 M.-H. Yang, D. J. Kriegman, and N. Ahuja, “Detecting faces in images: a survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 1, 2002.
 C. Kotropoulos and I. Pitas, “Rule-based face detection in frontal views,” 1997 IEEE Int. Conf. Acoust. Speech, Signal Process., vol. 4, 1997.
 C. Huang, H. Ai, Y. Li, and S. Lao, “High-performance rotation invariant multiview face detection.,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp. 671–686, 2007.
 E. Osuna, R. Freund, and F. Girosit, “Training support vector machines: an application to face detection,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 1997.
 H. A. Rowley, S. Baluja, and T. Kanade, “Rotation invariant neural network-based face detection,” Proceedings. 1998 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 1998.
 H. Schneiderman and T. Kanade, “A statistical method for 3D object detection applied to faces and cars,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. CVPR 2000 Cat NoPR00662, vol. 1, pp. 746–751, 2000.
 P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” Proc. 2001 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition. CVPR 2001, vol. 1, 2001.
 C. Zhang and Z. Zhang, “A Survey of Recent Advances in Face Detection,” Tech. Report, Microsoft Res., June, p. 17, 2010.
 R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, “Face detection in color images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 696–706, 2002.
 S. L. Phung, A. Bouzerdoum, and D. Chai, “A novel skin color model in ycbcr color space and its application to human face detection,” in Proceedings. International Conference on Image Processing, 2002, vol. 1, pp. 289–292.
 C. Lin, “Face detection in complicated backgrounds and different illumination conditions by using YCbCr color space and neural network,” Pattern Recognit. Lett., vol. 28, no. 16, pp. 2190–2200, 2007.
 Y. H. Chan and S. A. R. Abu-Bakar, “Face detection system based on feature-based chrominance colour information,” in Proceedings. International Conference on Computer Graphics, Imaging and Visualization, 2004. CGIV 2004., 2004, pp. 153–158.
 D. Ghimire and J. Lee, “A Robust Face Detection Method Based on Skin Color and Edges.,” JIPS, vol. 9, no. 1, pp. 141–156, 2013.
 J. de Dios and N. García, “Face detection based on a new color space YCgCr,” in International Conference on Image Processing, ICIP, 2003, vol. 3, pp. 111–909.
 K. Ghazali, J. Ma, and R. Xiao, “An Innovative Face Detection Based on YCgCr Color Space,” Phys. Procedia, vol. 25, pp. 2116–2124, 2012.
 M. Jones and P. Viola, “Fast multi-view face detection,” Mitsubishi Electr. Res. Lab TR-20003-96, 2003.
 M.-Q. Jing, “Novel face-detection method under various environments,” Opt. Eng., vol. 48, no. 6, p. 067202, Jun. 2009.
 J. Ma and G. Plonka, “The curvelet transform,” Signal Processing Magazine, IEEE, March, pp. 118–133, 2010.
 A. Majumdar and A. Bhattacharya, “A comparative study in wavelets, curvelets and contourlets as feature sets for pattern recognition.,” Int. Arab J. Inf. Technol., vol. 6, no. 1, pp. 47–51, 2009.
 E. Candès, L. Demanet, D. Donoho, and L. Ying, “Fast Discrete Curvelet Transforms,” Multiscale Modeling & Simulation, vol. 5, no. 3. pp. 861–899, 2006.
 I. Sumana and M. Islam, “Content based image retrieval using curvelet transform,” in 2008 IEEE 10th Workshop on Multimedia Signal Processing, 2008, pp. 11–16.
 E. Candes, L. Demanent, D. Donoho, and L. Ying, “CurveLab 2.1.2.” [Online]. Available: http://www.curvelet.org.
 A. Cretu and P. Payeur, “Biologically-inspired visual attention features for a vehicle classification task,” Int. J. Smart Sens. Intell. Syst., vol. 4, no. 3, pp. 402–423, 2011.
 X. Tian, H. Bao, C. Xu, and B. Wang, “Pedestrian Detection Algorithm based on Local Color Parallel Similarity Features,” Int. J. Smart Sens. Intell. Syst., vol. 6, no. 5, pp. 1869–1890, 2013.
 Z. Zhang, M. Wang, and Z. Lu, “A Skin Color Model Based on Modified GLHS Space,” J. Inf. Hiding Multimed. Signal Process., vol. 5, no. 2, pp. 144–151, 2014.
 T. Ikai, M. Ohka, and S. Kamiya, “Evaluation of finger direction recognition method for behavior control of Robot,” Int. J. Smart Sens. Intell. Syst., vol. 6, no. 5, pp. 2308–2333, 2013.
 J. M. Chaves-González, M. a. Vega-Rodríguez, J. a. Gómez-Pulido, and J. M. Sánchez-Pérez, “Detecting skin in face recognition systems: A colour spaces study,” Digit. Signal Process., vol. 20, no. 3, pp. 806–823, May 2010.
 L. Tao, Z. Shi, G. Ying, and G. Jing, “A circuit of configurable skin tone adjusting method base on exact skin color region detection,” in IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC), 2011, pp. 2–3.
 K. Sobottka and I. Pitas, “A novel method for automatic face segmentation, facial feature extraction and tracking,” Signal Process. Image Commun., vol. 12, pp. 263–281, 1998.
 M. Grgic and K. Delac, “FEI Face Database.” [Online]. Available: http://www.face-rec.org/databases.
 W. Tan and C. Chan, “A fusion approach for efficient human skin detection,” IEEE Trans. Ind. Informatics, vol. 8, no. 1, pp. 138–147, 2012.
 J. Wu, “Nonface.” [Online]. Available: http://c2inet.sce.ntu.edu.sg/Jianxin/RareEvent/nonface.zip.
 R. Frischholz, “Bao Face Database.” [Online]. Available: http://www.facedetection.com/Datasets.