Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Abstract

Interest in facial recognition hypotheses and algorithms has grown steadily over the last few decades. Video monitoring, criminal identification, building access control, and unmanned and autonomous vehicles are only a few examples of concrete applications that are becoming increasingly attractive to industry. Various techniques are being developed, including local, holistic, and hybrid approaches, which use only a few face image characteristics or the entire facial features to provide a face image description. Many methods have good results, if there are sufficiently representative training samples per person, in the face recognition system. Facial part finding and extraction show the utmost vital role in face and age recognition. In this research work a new algorithm is proposed for Face and Age Recognition (FAR) by using Discrete Wavelet Transform (DWT), Radial Basis Function Support Vector Machine (RBF-SVM) classifier, and Rotational Local Binary Pattern (RLBP). RLBP is utilized for the selection and extraction of features from the face image. In this algorithm, extract the face component like Nose, Mouth, Left and Right eye. In the preprocessing stage median filter is used to remove noises from the face image. By using this, there is an improvement in the feature extraction procedure. In pattern recognition, a basic errand is finding a picture from the picture parts. For the implementation of results FG-NET ((Face and Gesture Recognition Network) and AT&T datasets are used. The detection rate of face recognition has reached up to 92–98% and the detection rate for age recognition is 87%. The proposed algorithm is compared with SVM shows better over previous algorithms and also estimate the value of accuracy.

Keywords

Median filter radial basis function support vector machine discrete wavelet transform accuracy rotational local binary pattern

Get full access to this article

View all access options for this article.

References

Jain

Flynn

Ross

AA.

Handbook of biometrics. New York: Springer, 2008.

Jain

AK.

Handbook of face recognition. New York: Springer, 2011.

Ito

Kawai

Okano

, et al. Age and gender prediction from face images using convolutional neural network. In: APSIPA annual summit and conference, 12-15^th November 2018. Honolulu, HI, USA, USA.

Ramanathan

Chellappa

Face verification across age progression. IEEE Trans Image Process 2006; 15: 3349–3361.

Lanitis

Draganova

Christodoulou

Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern B Cybern 2004; 34: 621–628.

Guo

Huang

TS.

Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 2010; 32: 1955–1976.

Thukral

Mitra

Chellappa

A hierarchical approach for human age estimation. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2012, pp. 1529–1532. New York: IEEE.

Josephine Julina

Sree Sharmila

Facial recognition using histogram of gradients and support vector machines. In: ICCCSP-2017, 10-11 January 2017, pp. 1–5. Chennai, India.

Ingole

Karande

KJ.

Automatic age estimation from face images using facial features. In: Global conference on wireless computing and networking (GCWCN), 2018. New York: IEEE.

10.

Gong

Lin

Hidden factor analysis for age-invariant face recognition, 2013 IEEE International Conference on Computer Vision, pp. 2873–2879.

11.

Gong

Tao

, et al. Maximum entropy feature descriptor for age-invariant face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 5289–5297. New York: IEEE.

12.

Kalansuriya

Dharmaratn

AT.

Facial image classification based on age and gender. In: International conference on advances in ICT for emerging regions, 2013. New York: IEEE.

13.

Zhang

Xiang

Z-Z

, et al. Facial expression recognition based on salient patch selection. In: International conference on machine learning and cybernetics (ICMLC), Jeju, South Korea, 2016.

14.

Saikia

Kandali

AB.

DWT-ELBP based model for face recognition. In: International conference on energy, communication, data analytics, and soft computing, 2017. New York: IEEE.

15.

Chand

Saini

DD.

Rotation invariant face recognition using RLBP, LPQ and contourlet transform. Int Res J Eng Technol 2017; 4.

16.

Burges

CJC.

A tutorial on support vector machines for pattern recognition. Bell Laboratories, Lucent Technologies, June 1998.

17.

Kumar

Singh

FPR using machine learning with multi-feature method. IET Image Process 2018; 12: 1857–1865.