Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines

Lian, Huicheng; Lu, Bao-Liang

doi:10.1007/11760023_30

Cited by 107 publications

(79 citation statements)

References 9 publications

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“…Features based on local binary patterns (LBP) have also been used [10]. In this work, we use features derived from PCA since they have been successfully tested in previous literature.…”

Section: Feature Extractormentioning

confidence: 99%

Can Gender Be Predicted from Near-Infrared Face Images?

Ross

Chen

2011

Lecture Notes in Computer Science

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Abstract.Gender classification based on facial images has received increased attention in the computer vision literature. Previous work on this topic has focused on images acquired in the visible spectrum (VIS). We explore the possibility of predicting gender from face images acquired in the near-infrared spectrum (NIR). In this regard, we address the following two questions: (a) Can gender be predicted from NIR face images; and (b) Can a gender predictor learned using VIS images operate successfully on NIR images and vice-versa? Our experimental results suggest that NIR face images do have some discriminatory information pertaining to gender, although the degree of discrimination is noticeably lower than that of VIS images. Further, the use of an illumination normalization routine may be essential for facilitating cross-spectral gender prediction.

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“…Features based on local binary patterns (LBP) have also been used [10]. In this work, we use features derived from PCA since they have been successfully tested in previous literature.…”

Section: Feature Extractormentioning

confidence: 99%

Can Gender Be Predicted from Near-Infrared Face Images?

Ross

Chen

2011

Lecture Notes in Computer Science

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“…B.Moghaddam [11],Used support vector machine(SVM) with radial basis function kernels to classify from low resolution 12*12 "Thumbnail" faces .They used 1755 faces from FERET face database to evaluate the classifier and achieved Classification accuracy of 96%. R.Brunelli [12], used a set of 16 geometric features per image to train two competing networks with the radial basis function,one network for male and other for female and the classification rate was 79% on 168 training image show an error rate 21%. Hui-chang lain [13],presented Multi-view gender classification considering both shape and texture information to represent facial images.…”

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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“…Figure 2 below shows the 2D skeleton image created from different view angle at the same frame. This is useful to enhance the accuracy of the classification since some paper proposed using multi view image [10], [14]- [16]. However, these papers will only using one view for the analysis.…”

Section: Proposed Methodsmentioning

confidence: 99%

Human Gait Gender Classification using 3D Discrete Wavelet Transform Feature Extraction

Arai¹,

Asmara²

2014

IJARAI

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Abstract-Feature extraction for gait recognition has been created widely. The ancestor for this task is divided into two parts, model based and free-model based. Model-based approaches obtain a set of static or dynamic skeleton parameters via modeling or tracking body components such as limbs, legs, arms and thighs. Model-free approaches focus on shapes of silhouettes or the entire movement of physical bodies. Model-free approaches are insensitive to the quality of silhouettes. Its advantage is a low computational costs comparing to modelbased approaches. However, they are usually not robust to viewpoints and scale. Imaging technology also developed quickly this decades. Motion capture (mocap) device integrated with motion sensor has an expensive price and can only be owned by big animation studio. Fortunately now already existed Kinect camera equipped with depth sensor image in the market with very low price compare to any mocap device. Of course the accuracy not as good as the expensive one, but using some preprocessing method we can remove the jittery and noisy in the 3D skeleton points.Our proposed method is to analyze the effectiveness of 3D skeleton feature extraction using 3D Discrete Wavelet Transforms (3D DWT). We use Kinect Camera to get the depth data. We use Ipisoft mocap software to extract 3d skeleton model from Kinect video. From the experimental results shows 83.75% correctly classified instances using SVM.

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Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines

Cited by 107 publications

References 9 publications

Can Gender Be Predicted from Near-Infrared Face Images?

Can Gender Be Predicted from Near-Infrared Face Images?

Instant Human Face Attributes Recognition System

Human Gait Gender Classification using 3D Discrete Wavelet Transform Feature Extraction

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