Human face recognition with combination of DWT and machine learning

Abstract To enhance the accuracy of object recognition, various combination of recognition algorithms are used in recent literature. In this paper coherence of Discrete Wavelet Transform (DWT) is combined with four different algorithms: error vector of principal component analysis (PCA), eigen vector of PCA, eigen vector of Linear Discriminant Analysis (LDA) and Convolutional Neural Network (CNN) then combination of four results are done using entropy of detection probability and Fuzzy system. From this research the accuracy of recognition is found dependent on image and diversity of database. The combined method of the paper provides recognition rate of 89.56% for the worst case and 93.34% for the best case both can be said better in comparison with the previous works where individual method has been implemented on a specific set of images.

[1]  Wei-Yun Yau,et al.  Fusion of Appearance Image and Passive Stereo Depth Map for Face Recognition Based on the Bilateral 2DLDA , 2007, EURASIP J. Image Video Process..

[2]  Nikhil Buduma,et al.  Fundamentals of deep learning , 2017 .

[3]  Venugopal K R,et al.  Convolution Based Face Recognition Using DWT and HOG , 2018, 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).

[4]  Ting-Zhu Huang,et al.  Patch-Based Principal Component Analysis for Face Recognition , 2017, Comput. Intell. Neurosci..

[5]  Jayalakshmi Surendran,et al.  Wavelet transform coherence for magnitude and phase spectrum prediction from high frequency transient signals: Partial discharge in transformers , 2016, 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES).

[6]  Kohei Arai,et al.  Image Retrieval Method Utilizing Texture Information Derived from Discrete Wavelet Transformation Together with Color Information , 2016 .

[7]  Saruar Alam,et al.  Alzheimer disease classification using KPCA, LDA, and multi‐kernel learning SVM , 2017, Int. J. Imaging Syst. Technol..

[8]  Mohammad Mahdi Khalilzadeh,et al.  A new hybrid face recognition algorithm based on discrete wavelet transform and direct LDA , 2016, 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME).

[9]  Taghi M. Khoshgoftaar,et al.  Medicare fraud detection using neural networks , 2019, Journal of Big Data.

[10]  Sawon Pratiher,et al.  ECG signal analysis using wavelet coherence and s-transform for classification of cardiovascular diseases , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[11]  Qianhua He,et al.  Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System , 2018, International Journal on Computational Science & Applications.

[12]  Yong Xu,et al.  Face recognition using both visible light image and near-infrared image and a deep network , 2017, CAAI Trans. Intell. Technol..

[13]  Shubham Gupta,et al.  Analysis of Face Detection and Recognition Algorithms Using Viola Jones Algorithm with PCA and LDA , 2018, 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI).

[14]  Chee Onn Chow,et al.  High-density impulse noise detection and removal using deep convolutional neural network with particle swarm optimisation , 2019, IET Image Process..

[15]  Hassene Seddik,et al.  Robust Face Recognition Approaches Using PCA, ICA, LDA Based on DWT, and SVM Algorithms , 2018, 2018 41st International Conference on Telecommunications and Signal Processing (TSP).

[16]  Sanghyuk Lee,et al.  Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition , 2018, Cluster Computing.

[17]  Xingming Sun,et al.  Convolutional neural network for smooth filtering detection , 2018, IET Image Process..

[18]  Lina J. Karam,et al.  Quality Robust Mixtures of Deep Neural Networks , 2018, IEEE Transactions on Image Processing.

[19]  Sirion Vittayakorn,et al.  Facial expression recognition using local Gabor filters and PCA plus LDA , 2017, 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE).