Face Recognition Based on Wavelet Transform and Adaptive Local Binary Pattern

Face recognition has been extensively studied by many scholars in the recent decades. Local binary pattern (LBP) is one of the most popular local descriptors and has been widely applied to face recognition. Wavelet transform is also more and more active in the field of pattern recognition. In this paper, a novel feature extraction method is proposed to overcome illumination influence. First, a given face image is processed by the LBP operator, and an LBP image is obtained. Second, wavelet transform is used to extract discriminant feature from the LBP image. The experiment results on LFW, Extended YaleB and CMU-PIE face databases show that the proposed method outperforms several popular face recognition methods, and the preprocessing step plays an important role to extract effective features for classification.

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Gabor-scale binary pattern for face recognition

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691316500351 ◽

2016 ◽

Vol 14 (05) ◽

pp. 1650035 ◽

Cited By ~ 3

Author(s):

Zhi-Ming Li ◽

Zheng-Hai Huang ◽

Ting Zhang

Keyword(s):

Face Recognition ◽

Wavelet Transform ◽

Spatial Frequency ◽

Local Binary Pattern ◽

Experimental Results ◽

Gabor Wavelet ◽

Special Orientation ◽

Scale Ratio ◽

Face Descriptor ◽

Scale Length

In this paper, a novel face descriptor, the Gabor-scale binary pattern (GSBP), is proposed to explore the neighboring relationship in spatial, frequency and orientation domains for the purpose of face recognition. In order to extract the GSBP feature, the Gabor-scale volume and the Gabor-scale vector are introduced by using a group of Gabor wavelet coefficients with a special orientation. Moreover, the Gabor-scale length pattern and the Gabor-scale ratio pattern are proposed. Compared with the existed methods, GSBP utilizes the deep relations between neighboring Gabor subimages instead of directly combining Gabor wavelet transform and local binary pattern. For estimating the performance of GSBP, we compare the proposed method with the related methods on several popular face databases, including LFW, FERET, AR, Yale and Extended YaleB databases. The experimental results show that the proposed method outperforms several popular face recognition methods.

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

International Journal of Electrical Engineering Education ◽

10.1177/0020720920988489 ◽

2021 ◽

pp. 002072092098848

Author(s):

Ajit Singh ◽

Chander Kant

Keyword(s):

Support Vector Machine ◽

Face Recognition ◽

Wavelet Transform ◽

Basis Function ◽

Detection Rate ◽

Local Binary Pattern ◽

Face Image ◽

Support Vector ◽

Discrete Wavelet ◽

The Face

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.

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Statistical Evaluation of Emerging Feature Extraction Techniques for Aging-Invariant Face Recognition Systems

FUOYE Journal of Engineering and Technology ◽

10.46792/fuoyejet.v2i1.92 ◽

2017 ◽

Vol 2 (1) ◽

Author(s):

Ayodele Oloyede ◽

Temitayo Matthew Fagbola ◽

Stephen Olabiyisi ◽

Elijah Omidiora ◽

John Oladosu

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Wavelet Transform ◽

Local Binary Pattern ◽

Statistical Significance ◽

Significance Test ◽

Gabor Wavelet ◽

Extraction Techniques ◽

Statistical Significance Test ◽

Recognition Systems

Large variation in facial appearances of the same individual makes most baseline Aging-Invariant Face Recognition Systems (AI-FRS) suffer from high automatic misclassification of faces. However, some Aging-Invariant Feature Extraction Techniques (AI-FET) for AI-FRS are emerging to help achieve good recognition results when compared to some baseline transformations in conditions with large amount of variations in facial texture and shape. However, the performance results of these AI-FET need to be further investigated statistically to avoid being misled. Statistical significance test can be used to logically justify such performance claims. The statistical significance test would serve as a decision rule to determine the degree of acceptability of the probability to make a wrong decision should such performance claims be found faulty. In this paper, the means between the quantitative results of emerging AI-FET (Histogram of Gradient (HoG), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and Local Binary Pattern-Gabor Wavelet Transform (LBP-GWT)) and the baseline aging-invariant techniques (Local Binary Pattern (LBP) and Gabor Wavelet Transform (GWT)) were computed and compared to determine if those means are statistically significantly different from each other using one-way Analysis of Variance (ANOVA). The ANOVA results obtained at 0.05 critical significance level indicate that the results of the emerging AI-FET techniques are not statistically significantly different from those of baseline techniques because the F-critical value was found to be greater than the value of the calculated F-statistics in all the evaluations conducted.

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Wavelet-Local binary pattern based face recognition

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v16i1.5779 ◽

2017 ◽

Vol 16 (1) ◽

pp. 7552-7556

Author(s):

Azad Abdullah Ameen ◽

Hardi M. M-Saleh ◽

Zrar Kh. Abdul

Keyword(s):

Face Recognition ◽

Wavelet Transform ◽

Discriminant Analysis ◽

Statistical Learning ◽

Local Binary Pattern ◽

Original Image ◽

Face Database ◽

Resultant Data ◽

Improved Accuracy

Over the last twenty years face recognition has made immense progress based on statistical learning or subspace discriminant analysis. This paper investigates a technique to reduce features necessary for face recognition based on local binary pattern, which is constructed by applying wavelet transform into local binary pattern. The approach is evaluated in two ways: wavelet transform applied to the LBP features and wavelet transform applied twice on the original image and LBP features. The resultant data are compared to the results obtained without applying wavelet transform, revealing that the reduction base one wavelet achieves the same or sometimes improved accuracy. The proposed algorithm is experimented on the Cambridge ORL Face database.

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