Abstract
Nowadays, multiple face detection (MFD) and extraction play an important role in face identification for various applications. In the proposed algorithm, Support Vector Machine (SVM) has been used for multiple face detection, and Discrete Wavelet Transform (DWT), Edge Histogram (EH), and Auto-correlogram (AC) are used for feature extraction. The proposed methodology worked on two different database i.e. Carnegie Mellon University (CMU) and BAO database for MFD. In this research paper, the proposed methodology gives a better result than the existing technique. Finally, our accuracy raised up to 90% approximately.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kang, S., Choi, B., Jo, D.: Faces detection method based on skin color modeling. J. Syst. Archit. 64, 100–109 (2016)
Kumar, S., Singh, S., Kumar, J.: A study on face recognition techniques with age and gender classification. In: IEEE International Conference on Computing, Communication and Automation (ICCCA), 5, 6 May 2017
Kumar, S., Singh, S., Kumar, J.: A comparative study on face spoofing attacks. In: IEEE International Conference on Computing, Communication and Automation (ICCCA), 5, 6 May 2017
Kumar, S., Singh, S., Kumar, J.: Automatic face detection using a genetic algorithm for various challenges. Int. J. Sci. Res. Mod. Educ. 2(1), 197–203 (2017)
See, Y.C., Noor, N.M., Lai, A.C.: Hybrid face detection with skin segmentation and edge detection. In: 2013 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 406–411 (2013)
Shah, J.H., Sharif, M., Yasmin, M., Fernandes, S.L.: Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recognit. Lett. 1–17 (2017)
Zhou, X., Jin, K., Chen, Q., Xu, M., Shang, Y.: Multiple face tracking and recognition with identity-specific localized metric learning. Pattern Recognit. 75, 41–50 (2018)
Mahajan, J.A., Paithane, A.N.: Face detection on distorted images by using quality HOG features. In: 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 439–444 (2017)
Nasr, S., Bouallegue, K., Shoaib, M., Mekki, H.: Face recognition system using a bag of features and multi-class SVM for robot applications. In: 2017 International Conference on Control, Automation and Diagnosis (ICCAD), pp. 263–268 (2017)
Tao, Q.Q., Zhan, S., Li, X.H., Kurihara, T.: Robust face detection using local CNN and SVM based on kernel combination. Neurocomputing 211, 98–105 (2016)
Julina, J., Kulandai J., Sree Sharmila, T.: Facial recognition using a histogram of gradients and support vector machines. In: IEEE International Conference on Computer, Communication and Signal Processing (ICCCSP), pp. 1–5 (2017)
Naik, N., Rathna, G.N.: Robust Real-Time Face Recognition and Tracking on GPU Using a Fusion of RGB and Depth Image. arXiv preprint. arXiv:1504.01883 (2015)
Yadav, S., Nain, N.: Fast face detection based on skin segmentation and facial features. In: 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 663–668. IEEE (2015)
Ramachandra, R., Yang, B., Raja, K.B., Busch, C.: A new perspective—face recognition with the light-field camera. In: IEEE International Conference on Biometrics (ICB), pp. 1–8. IEEE (2013)
Jensen, O.H.: Implementing the Viola-Jones face detection algorithm. Master’s thesis, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark (2008)
Chaudhary, M.D., Upadhyay, A.B.: Integrating shape and edge histogram descriptor with stationary wavelet transform for effective content-based image retrieval. In: 2014 International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1522–1527. IEEE (2014)
Masselos, K., Andreopoulos, Y., Stouraitis, T.: Performance comparison of two-dimensional discrete wavelet transforms computation schedules on a VLIW digital signal processor. IEE Proc. Vis. Image Signal Process. 153(2), 173–180 (2006)
Smith, J.R., Chang, S.F.: Tools and techniques for color image retrieval. In: Storage and Retrieval for Still Image and Video Databases IV, vol. 2670, pp. 426–438. International Society for Optics and Photonics, Washington (1996)
Yang, S., Bebis, G., Chu, Y., Zhao, L.: Effective face recognition using a bag of features with additive kernels. J. Electron. Imag. 25(1), 013025 (2016)
Huang, Z.C., Chan, P.P., Ng, W.W., Yeung, D.S.: Content-based image retrieval using color moment and Gabor texture feature. In: 2010 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 719–724. IEEE (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kumar, S., Singh, S., Kumar, J. (2019). Multiple Face Detection Using Hybrid Features with SVM Classifier. In: Jain, L., E. Balas, V., Johri, P. (eds) Data and Communication Networks. Advances in Intelligent Systems and Computing, vol 847. Springer, Singapore. https://doi.org/10.1007/978-981-13-2254-9_23
Download citation
DOI: https://doi.org/10.1007/978-981-13-2254-9_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2253-2
Online ISBN: 978-981-13-2254-9
eBook Packages: EngineeringEngineering (R0)