Local Binary Patterns as an Image Preprocessing for Face Authentication

Heusch, Guillaume; Rodriguez, Yann; Marcel, Sébastien

doi:10.1109/fgr.2006.72

Cited by 138 publications

(91 citation statements)

References 20 publications

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“…Len Bui, Dat Tran [7],presented novel method for gender recognition by using 2D principal component analysis for extracting the feature vector and combine these 2D PCA with Support vector machine (SVM) discriminative method for classification. Experiments for this approach have been conducted on FERET data set.…”

Section: B Gender Classification: -mentioning

confidence: 99%

Instant Human Face Attributes Recognition System

Bellustin¹,

Kalafati²,

Kovalchuck³

et al. 2011

SpecialIssue

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Abstract-The objective of this work is to provide a simple and yet efficient tool for human attributes like gender, age and ethnicity by the human facial image in the real time image as we all aware this term that "Real-Time frame rate is a vital factor for practical deployment of computer vision system". In this particular paper we are trying to presents the progress towards face detection and human attributes classification system. We have developed an algorithm for the classification of gender, age and race from human frontal facial image As the basis of the classifier proposed algorithm uses training set neuron receptors that process visual information a study of the several variants of these classifiers and shows the principal possibility of sex determination, assessment of a person's age on a scale (adultchildren) and recognition of race by using the neuron-like receptors.

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Section: B Gender Classification: -mentioning

confidence: 99%

Instant Human Face Attributes Recognition System

Bellustin¹,

Kalafati²,

Kovalchuck³

et al. 2011

SpecialIssue

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“…We attempt to classify each of the prototypical expressions at 5 different yaw angles (0,30,45,60,90). LBPs have yielded accurate results with face recognition [6] and more recently with frontal facial expressions [4,5,9,23]. We apply the LBP operator and its variants to the BU-3DEF database and present our findings.…”

Section: Introductionmentioning

confidence: 99%

The effects of Pose on Facial Expression Recognition

Moore¹,

Bowden²

2009

Procedings of the British Machine Vision Conference 2009

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Research into facial expression recognition has predominantly been based upon near frontal view data. However, a recent 3D facial expression database (BU-3DFE database) has allowed empirical investigation of facial expression recognition across pose. In this paper, we investigate the effects of pose from frontal to profile view on facial expression recognition. Experiments are carried out on 100 subjects with 5 yaw angles over 6 prototypical expressions. Expressions have 4 levels of intensity from subtle to exaggerated. We evaluate features such as local binary patterns (LBPs) as well as various extensions of LBPs. In addition, a novel approach to facial expression recognition is proposed using local gabor binary patterns (LGBPs). Multi class support vector machines (SVMs) are used for classification. We investigate the effects of image resolution and pose on facial expression classification using a variety of different features.

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“…Other techniques are based on constraining and modeling the appearance of the face on the image, both as shape and texture information. Several methods have been based on the extraction and classification of salient facial features by means of multi-scale filtering with Gabor kernels [20,4,21,9]. Along this direction, the techniques based on the estimation and progressive warping of a "morphable face model" explicitly derive a constrained mapping between the 3D face and its two-dimensional appearance on the image [16].…”

Section: Introductionmentioning

confidence: 99%

“…One of the interesting features of the SIFT approach is the capability to capture the main graylevel features of an object's view by means of local patterns extracted from a scale-space decomposition of the image. In this respect, the SIFT approach is similar to the Local Binary Patterns method [21,9], with the difference of producing a view-invariant representation of the extracted 2D patterns.…”

Section: Introductionmentioning

confidence: 99%