Gabor-scale binary pattern for face recognition

2016 ◽  
Vol 14 (05) ◽  
pp. 1650035 ◽  
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.

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

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.

scholarly journals The Berkeley Wavelet Transform: A Biologically Inspired Orthogonal Wavelet Transform

2008 ◽  
Vol 20 (6) ◽  
pp. 1537-1564 ◽  
Author(s):  
Ben Willmore ◽  
Ryan J. Prenger ◽  
Michael C.-K. Wu ◽  
Jack L. Gallant
Keyword(s):  
Wavelet Transform ◽  
Spatial Frequency ◽  
Orthonormal Basis ◽  
Receptive Fields ◽  
Constant Term ◽  
Two Dimensions ◽  
Gabor Wavelet ◽  
Scale Invariant ◽  
V1 Neurons ◽  
Spatiotemporal Receptive Fields

We describe the Berkeley wavelet transform (BWT), a two-dimensional triadic wavelet transform. The BWT comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and the other has even symmetry. By translation and scaling of the whole set (plus a single constant term), the wavelets form a complete, orthonormal basis in two dimensions. The BWT shares many characteristics with the receptive fields of neurons in mammalian primary visual cortex (V1). Like these receptive fields, BWT wavelets are localized in space, tuned in spatial frequency and orientation, and form a set that is approximately scale invariant. The wavelets also have spatial frequency and orientation bandwidths that are comparable with biological values. Although the classical Gabor wavelet model is a more accurate description of the receptive fields of individual V1 neurons, the BWT has some interesting advantages. It is a complete, orthonormal basis and is therefore inexpensive to compute, manipulate, and invert. These properties make the BWT useful in situations where computational power or experimental data are limited, such as estimation of the spatiotemporal receptive fields of neurons.

Face Recognition using Multi Region Prominent LBP Representation

2016 ◽  
Vol 6 (6) ◽  
pp. 2781 ◽  
Author(s):  
Srinivasa Reddy Konda ◽  
Vijaya Kumar V ◽  
Venkata Krishna
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Fundamental Property ◽  
Local Binary Patterns ◽  
Experimental Results ◽  
The Other ◽  
Complex Task ◽  
Rectangular Region ◽  
Multiple Regions ◽  
Uniform Local Binary Pattern

<p>Various face recognition methods are derived using local features among them the Local Binary Pattern (LBP) approach is very famous. The histogram techniques based on LBP is a complex task. Later Uniform Local Binary Pattern (ULBP) is derived on LBP, based on the bitwise transitions and ULBP’s are treated as the fundamental property of texture. The ULBP approach treated all Non-Uniform Local Binary Patterns’ (NULBP) into one miscellaneous label. Recently we have derived Prominent LBP (PLBP), Maximum PLBP (MPLBP) and Smallest PLBP (SPLBP). The PLBP consists of the majority of the ULBP’s and some of the NULBP’s. The basic disadvantage of these various variants of LBP’s  is they are basically local approaches and completely failed in representing features derived from large regions or macrostructures, which are very much essential for faces. This paper derives PLBP’s on the large region. The rectangular region of this paper is assumed with a size of multiples of three and PLBPs are evaluated on dividing each region into multiple regions. The proposed Multi Region-PLBP (MR-PLBP) approach is tested on three facial databases namely Yale, Indian and AT&amp;T ORL. The experimental results show the proposed approach significantly outperforms the other LBP based face recognition methods.</p>

A Feature Extraction Method based on Local Binary Pattern Preprocessing and Wavelet Transform

2020 ◽  
Vol 34 (13) ◽  
pp. 2050030
Author(s):  
Peng-Yi Liu ◽  
Zhi-Ming Li
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Face Image ◽  
Local Descriptors ◽  
Feature Extraction Method

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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