Mongoloid and Non-Mongoloid Race Classification from Face Image Using Local Binary Pattern Feature Extractions

In this paper, we propose two self-adapting patch strategies, which are obtained by employing the integral projection technique on images’ edge images, while the edge images are recovered by the two-dimensional discrete wavelet transform. The patch strategies are equipped with the advantage of considering the single image’s unique properties and maintaining the integrity of some particular local information. Combining the self-adapting patch strategies with local binary pattern feature extraction and the classifier of the forward and backward greedy algorithms under strong sparse constraint, we propose two new face recognition methods. Experiments are run on the Georgia Tech, LFW and AR face databases. The obtained numerical results show that the new methods outperform some related patch-based methods to a larger extent.

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HyTexiLa: High Resolution Visible and Near Infrared Hyperspectral Texture Images

Sensors ◽

10.3390/s18072045 ◽

2018 ◽

Vol 18 (7) ◽

pp. 2045 ◽

Cited By ~ 11

Author(s):

Haris Khan ◽

Sofiane Mihoubi ◽

Benjamin Mathon ◽

Jean-Baptiste Thomas ◽

Jon Hardeberg

Keyword(s):

Spectral Analysis ◽

High Resolution ◽

Matrix Factorization ◽

Local Binary Pattern ◽

Near Infrared ◽

Texture Classification ◽

Hyperspectral Images ◽

Close Range ◽

Local Binary Pattern Feature ◽

Non Negative Matrix Factorization

We present a dataset of close range hyperspectral images of materials that span the visible and near infrared spectrums: HyTexiLa (Hyperspectral Texture images acquired in Laboratory). The data is intended to provide high spectral and spatial resolution reflectance images of 112 materials to study spatial and spectral textures. In this paper we discuss the calibration of the data and the method for addressing the distortions during image acquisition. We provide a spectral analysis based on non-negative matrix factorization to quantify the spectral complexity of the samples and extend local binary pattern operators to the hyperspectral texture analysis. The results demonstrate that although the spectral complexity of each of the textures is generally low, increasing the number of bands permits better texture classification, with the opponent band local binary pattern feature giving the best performance.

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Single Sample Face Recognition Based on Global Local Binary Pattern Feature Extraction

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-70136-3_56 ◽

2017 ◽

pp. 530-539

Author(s):

Meng Zhang ◽

Li Zhang ◽

Chengxiang Hu

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Local Binary Pattern ◽

Single Sample ◽

Local Binary Pattern Feature

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Curvelet Transform-Local Binary Pattern Feature Extraction Technique for Mass Detection and Classification in Digital Mammogram

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2018/42579 ◽

2018 ◽

Vol 28 (3) ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Adeyemo Tosin ◽

Adepoju Morufat ◽

Oladele Omotayo ◽

Wahab Bolanle ◽

Omidiora Olusayo ◽

...

Keyword(s):

Feature Extraction ◽

Local Binary Pattern ◽

Curvelet Transform ◽

Extraction Technique ◽

Digital Mammogram ◽

Mass Detection ◽

Local Binary Pattern Feature ◽

Feature Extraction Technique

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Research of Face Recognition Methods Based on Binding Feature Extraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.668 ◽

2014 ◽

Vol 568-570 ◽

pp. 668-671

Author(s):

Yi Long ◽

Fu Rong Liu ◽

Guo Qing Qiu

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Extraction Method ◽

Local Binary Pattern ◽

Feature Vector ◽

Recognition Rate ◽

Principal Component ◽

Face Image ◽

Feature Extraction Method ◽

Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

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