Multi-Human Face Detection Using Gabor Filters and Neural Networks in Internet Images
Subject Areas : electrical and computer engineeringR. Mohammadian 1 * , M. Mahlouji 2
1 - دانشگاه آزاد اسلامی، واحد كاشان
2 -
Keywords: Gabor energy vertical projection median filter facial feature,
Abstract :
This paper presents a new method for multi human face detection from frontal view in internet images with complex background. The main goal is to reduce false acceptance error rate using feed forward back propagation multilayer perceptron neural network and Gabor energy feature in the frequency domain. In the proposed method, the false acceptance error extremely decreased using a combination of three operations; introducing a new preprocessing algorithm to increase the quality of Gabor energy feature, performing two step monitoring on the input and output images, and utilizing three indexes of facial components recognition in Gabor energy output. In this paper, a new image database namely RFD is collected from internet images including 583 non repetitive face images and 9961 non face images with size of 192×168. The face detection accuracy of the proposed method on RFD images is 88.16% with false acceptance rate of 0.48% or 48 false acceptances only, while Viola-Jones algorithm has 124 false acceptances. Therefore, the false acceptance error of the proposed method has reduced by 2.5 times compared to that of Viola-Jones algorithm.
[1] N. Rathore and D. Chaubey, "A survey on face detection and recognition," International J. of Computer Architecture and Mobility, vol. 1, no. 5, 6 pp., Mar. 2013.
[2] C. Zhang and Z. Zhang, A Survey of Recent Advances in Face Detection, Technical Report, Microsoft Research, Jun. 2010.
[3] A. Lumini, B. Nanni, and F. Dominio, "Effective and precise face detection based on both gray-level image and depth map," Applied Computing and Informatics, Apr. 2014.
[4] D. Kriegman, M. Yang, and N. Ahuja, "Detecting faces in images: a survey," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002.
[5] S. Kim, S. –K Kim, K. Toh, Y. Ban, and S. Lee, "Face detection based on skin color likelihood," Pattern Recognition, vol. 47, no. 4, pp. 1573-1585, Apr. 2014.
[6] X. Zhang, J. Yan, and Z. Lei, "Face detection by structural models," Image and Vision Computing, 2013.
[7] R. Pereira de Magalhaes and C. Lima, "A new method for Haar-Like features weight adjustment using principal component analysis for face detection," in Proc. of the 9th Int. Conf. on Systems, pp. 55-62, 2014.
[8] M. Gopi Krishna and A. Srinivasulu, "Face detection system on AdaBoost algorithm using Haar classifiers," International J. of Modern Engineering Research, IJMER, vol. 2, no. 5, pp. 3556-3560, Sep./Oct. 2012.
[9] S. Ulukaya, C. E. Erdem, and A. Karaali, "Combining Haar feature and skin color based classifiers for face detection," in Proc. of IEEE IEEE Int, Conf. on Acoustics, Speech and Signal Processing, ICASSP'11, pp. 1497-1500, Prague, Czech Republic, 22-27 May 2011.
[10] T. Wang, J. Li, and Y. Zhang, "Face detection using SURF cascade," in Proc. IEEE Int. Conf. on Computer Vision Workshops, pp. 2183-2190, Barcelona, Spain 6-13 Nov. 2011.
[11] D. Kim and R. Dahyot, "Face components detection using SURF descriptors and SVMs," in Proc. Int. Machine Vision and Image Processing Conf., IMVIP '08, pp. 51-56, Portrush, UK, 3-5 Sep. 2008.
[12] D. Huang et al., "Local binary patterns and its application to facial image analysis: A survey," IEEE Trans. on Systems, Man, and Cybernetis, vol. 41, no. 6, pp. 765-781, Nov. 2011.
[13] Y. Zhu, H. Pan, and L. Xia, "Efficient and accurate face detection using heterogeneous feature descriptors," Computer Vision and Image Understanding, vol. 117, no. 1, pp. 12-28, Jan. 2013.
[14] M. Fische, H. Ekenel, F. Jiang, and E. Shi, "Combining texture and stereo disparity cues for real-time face detection," Signal Processing: Image Communication, vol. 28, no. 9, pp. 1100-1113, Oct. 2013.
[15] A. Shimizu, H. Kobatake, and L. Huang, "Robust face detection using Gabor filter features," Pattern Recognition Letters, vol. 26, no. 11, pp. 1641-1649, Aug. 2005.
[16] S. Prakash Sahu, "Face detection by fine tuning the Gabor filter parameter," International J. of Computer Science and Information Technologies, vol. 2, no. 6, pp. 2719-2724, Nov./Dec 2011.
[17] L. Xiaohua, K. Lam, S. Lansun, and Z. Jiliu, "Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers," Pattern Recognition Letters, vol. 30, no. 8, pp. 717-728, Jun. 2009.
[18] N. Batool and R. Chellappa, "Fast detection of facial wrinkles based on Gabor features using image morphology and geometric constraints," Pattern Recognition, vol. 48, no. 3, pp. 642-658, Mar. 2015.
[19] M. Gaianu and D. M. Onchis, "Face and marker detection using Gabor frames on GPUs," Signal Processing, vol. 96, pp. 90-93, Mar. 2014.
[20] م. محلوجی و ر. محمدیان، "آشکارسازی صورت با استفاده از فیلترهای گابور و شبکههای عصبی،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، سال 13، شماره 1- ب، صص. 13-1، تابستان 1394.
[21] S. Kaneda, R. Ariizumi, and H. Haga, "Energy saving of TV by face detection," Pervasive Technologies Related to Assistive Environments, 2008.
[22] W. Eric, E Wong, and Tingshao, "Computer power management system based on the face detection," in Computer Engineering and Networking: Springer International Publishing, pp. 913-920, 2014.
[23] P. Viola and M. J. Jones, "Robust real-time face detection," International J. of Computer Vision, vol. 57, no. 2, pp. 137-154, May 2004.
[24] S. Adwan and H. Arof, "Modified integral projection method for eye detection using dinamic time warping," International J. of Innovation Computing, Information, and Control, vol. 8, no. 1-A, pp. 187-189, Jan. 2012.
[25] J. Meynet, V. Popovici, and J. Thiran, "Face detection with boosted Gaussian features," Pattern Recognition, vol 40, no. 8, pp. 2283-2291, Aug. 2007.
[26] S. Shan, X. Chen, S. Yan, and W. Gao, "Locally assembled binary (LAB) feature with feature-centric cascade for fast and accurate face detection," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'08, 7 pp., Anchorage, AK, USA, 23-28 Jun. 2008.