Research on Adaptive Face Gender Recognition Based on Compressive Sensing

Abstract:

An adaptive gender recognition method is proposed in this paper. At first, do multiwavlet transform to face image and get its low frequency information, then do feature extraction to the low frequency information using compressive sensing (CS), use extreme learning machine (ELM) to achieve gender recognition finally. In the process of feature extraction, we use genetic algorithm (GA) to get the number of measurements of CS in order to gain the highest recognition rate, so the method can adaptive access optimal performance. Experimental results show that compared with PDA and LDA, the new method improved the recognition accuracy substantially.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 4187-4190
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.4187
• Online since: July 2014
• Authors: Lin Zhang*
• Keywords: Adaptive, Compressive Sensing, Feature Extraction, Gender Recognition

References

[1] X.Q. Chen, Z.Y. Xu, L. Lu and P.F. Shi: Gender Recognition Based on Face Features, Journal of Data Acquisition & Processing, Vol. 25 (2010), pp.595-599.

[2] X. Bai: Gender recognition research based on face image, thesis of master degree, Taiyuan University of Science & technology, (2013).

[3] W.W. Pei: Gender Studier Based on the Feather Extraction of Face Images Analysis, thesis of master degree, Taiyuan University of Technology, (2010).

[4] T.Q. Zhang: Multi-view Face Gender Classification and Feature Selection, thesis of master degree, Shanghai Jiao Tong University, (2010).

[5] Z. Yang: Research on the Technology of Gender Recognition Based on Face Image, thesis of master degree, Jiang Su University, (2009).

[6] N. Sun, C. Zhang, X.Y. Zhu, R. Dai and S. Wei: Survey of face gender classification using compressed sensing, COMPUTER ENGINEERING AND DESIGN, Vol. 33 (2011), pp.4382-4385.

[7] E. Candès and M. Wakin: An introduction to compressive sampling, IEEE Signal Processing Magazine, Vol. 25 (2008), pp.21-30.

DOI: 10.1109/msp.2007.914731

[8] F. Yang: Research on Image Reconstruction of Compressive Sensing, thesis of master degree, Jiangxi Science & Technology Normal University, (2010).

[9] M. Zhai, R. Zhang and Y. Wang: A Summary of Network Structure Adjustments of Extreme Learning Machine, Journal of Xi'an University of Arts & Science (Nat Sci Ed), Vol. 17 (2014), pp.1-6.

[10] G.B. Huang, Y.Z. Qin and C.K. Siew: Extreme learning machine: theory and applications, Neurocomputing, Vol. 70 (2006), pp.489-501.

DOI: 10.1016/j.neucom.2005.12.126

[11] G.B. Huang, D.H. Wang and Y. Lan: Extreme learning machines: a survey, International Journal of Machine Learning and Cybernetics, Vol. 2 (2011), pp.107-122.

[12] G.B. Huang, H.M. Zhou, X.J. Ding and R. Zhang: Extreme Learning Machine for Regression and Multiclass Classification, ieee transactions on systems, man and cybernetics, part b: cybernetics, Vol. 42 (2012), pp.513-529.

DOI: 10.1109/tsmcb.2011.2168604

[13] H.F. KE and J. Ying: Real-time license character recognition technology based on R-ELM, Journal of Zhejiang University (Engineering Science), Vol. 48 (2014), pp.1-8.

[14] P.Y. Fei and B.L. Guo: A study on multiwavelet based image denoising, Journal of Image and Graphics, Vol. 10 (2005), pp.107-112.

[15] L. Zhang, Z.J. Fang, S.Q. Wang, F. Yang, G.D. Liu: Multiwavelet Adaptive Denoising Method Based on Genetic Algorithm, Vol. 28 (2009), pp.77-80.

DOI: 10.3724/sp.j.1010.2009.00077