Modifications to the Eigenphases Method for Face Recognition Based on SVM

Olivares-Mercado, Jesús; Toscano-Medina, Karina; Sánchez-Pérez, Gabriel; Nakano-Miyatake, Mariko

doi:10.1016/j.riit.2016.01.011

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…(3) Calculate the mean and mean square error of face contour gray and use formula (8) to grayscale transform the face image…”

Section: Normalized Processingmentioning

confidence: 99%

“…In recent years, on the basis of careful research on feature face technology, domestic scholars have tried to combine feature extraction method based on feature face with various back-end classifiers and proposed various improved versions or extended algorithms. The main research contents include linear/nonlinear discriminant analysis [6], Bayesian probability model [7], support vector machine (SVM) [8], artificial neural network (NN) [9], and intra/intraclass dual subspace analysis method [10].…”

Section: Introductionmentioning

confidence: 99%

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Research on Intelligent Algorithm of Identity Authentication Based on Facial Features

Yuan

Wang

et al. 2021

Wireless Communications and Mobile Computing

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Identity recognition is a research hotspot in the information age. Nowadays, more and more occasions require identity recognition, especially in smart home. Identity recognition of the head of the household can avoid many troubles, such as home identification and network information authentication. Nowadays, in smart home identification, especially based on face recognition, system authentication is basically through feature matching. Although this method is convenient and quick to use, it lacks intelligence. Nowadays, for the make-up, facelift, posture, and other differences, the accuracy of the system is greatly reduced. In this paper, the face recognition method is used for identity authentication. Firstly, the AdaBoost learning algorithm is used to construct the face detection and eye detection classifier to realize the detection and localization of the face and eyes. Secondly, the two-dimensional discrete wavelet transform is used to extract facial features and construct a personal face dynamic feature database. Finally, an improved elastic template matching algorithm is used to establish an intelligent classification method for dynamic face elasticity models. The simulation shows that the proposed method can intelligently adapt to various environments without reducing the accuracy.

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“…(3) Calculate the mean and mean square error of face contour gray and use formula (8) to grayscale transform the face image…”

Section: Normalized Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Algorithm of Identity Authentication Based on Facial Features

Yuan

Wang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Reference [6] established a new algorithm by modifying the eigenphase method for face recognition based on support vector machine (SVM). The authors performed two phases to increase accuracy.…”

Section: Introductionmentioning

confidence: 99%

Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

Alazzawi

Uçan

Bayat

2018

Mathematical Problems in Engineering

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Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.

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Modifications to the Eigenphases Method for Face Recognition Based on SVM

Olivares-Mercado

Toscano-Medina

Sánchez-Pérez

et al. 2016

Ingeniería, Investigación y Tecnología

Self Cite

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This paper presents two modifications to the eigenphases method to increase its accuracy. In the first modification, called Local Spatial Domain Eigenphases (LSDE), the face image is first segmented into blocks of N × N pixels, whose magnitudes are normalized. These blocks are then concatenated before the phase spectrum estimation, and finally Principal Component Analysis (PCA) is used for dimensionality reduction. In the second modification, called Local Frequency Domain Eigenphases (LFDE), first the face image is segmented into blocks of pixels, whose pixels are normalized. The phase spectrum of each block is estimated independently. Next, the phase spectra of all the blocks are concatenated and then are applied to the PCA stage for dimensionality reduction. The proposed approaches are evaluated using open-set and closed-set face identification, as well as identity verification, using the "AR Face Database." The evaluation results show that the proposed modifications, using the Support Vector Machine as the classifier, perform fairly well under different illumination and partial occlusion conditions.

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Modifications to the Eigenphases Method for Face Recognition Based on SVM

Cited by 5 publications

References 19 publications

Research on Intelligent Algorithm of Identity Authentication Based on Facial Features

Research on Intelligent Algorithm of Identity Authentication Based on Facial Features

Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

Modifications to the Eigenphases Method for Face Recognition Based on SVM

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