Face Gender Recognition Research Based on Local Features and Support Vector Machine

Lin Zhang

doi:10.4028/www.scientific.net/AMM.687-691.3714

Paper Titles

Image Segmentation of Pig Using Wavelet Modulus and Edge Growth
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Optimization Algorithm Analysis on Satellite Communication Signal Processing
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Multi-View Radar Target Recognition Based on Bayesian Compressive Sensing
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The Selection of Transmitter Elevation in a Ka-Band HAP-Based Communication System under Fog Attenuation
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Face Gender Recognition Research Based on Local Features and Support Vector Machine
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Design Analysis of Visual Illusion Application in Visual Communication
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Simulation Study on Network Dynamic Recognition of Movement Injury Risk Based on Computer Visualization Technology
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Image Reconstruction Algorithm of Track and Field Action Based on 3D Virtual Animation
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Massive Image Treatment System Based on Cloud Computing Platform
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Face Gender Recognition Research Based on Local...

Face Gender Recognition Research Based on Local Features and Support Vector Machine

Abstract:

In this paper, we proposed a face gender recognition method based on local features and SVM. First, we divide the face image into five parts which are used to instead of the whole face for better recognition performance. Second, we use CS to extract local features of these five parts. Then, we respectively train five single SVM classifiers to achieve one to one feature recognition for local features. Finally, decision information fusion is used to achieve the final classification. Because SVM were successfully used to solve numerous pattern recognition problems and is mainly used to solve two-classification problem, selecting SVM to do gender recognition in our method has the obvious superiority. After a lot of experiments, results show that the proposed method in this paper is stable and effective, greatly improving the efficiency of face gender recognition.