Age Classification based on Corner Pixel Grey Level Co-Occurrences Matrix (CP-GLCM) of TN-LBP

Kumar, Pullela S V V S R; Murthy, J. V. R.; Srinidhi, V.

doi:10.5120/15780-4480

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The age groups were determined using, various algorithms like Radial Basis Function (RBF), presented by (Yazdi et al, 2012), Back Propagation Network presented by (Mehdi et al, 2009) and Support Vector Machine (SVM)-Sequential Minimum Optimization (SMO), which includes mathematical techniques related to facial features. A combination of Discrete Wavelet Transform and Gradient Orientation pyramid for extracting the facial features was proposed by (Saeid and Leila, 2012).…”

Section: Previous Workmentioning

confidence: 99%

A Gaussian Mixture Model for Classifying the Human Age Using DWT and Sammon Map

Nithyashri¹,

Kulanthaivel²

2014

Journal of Computer Science

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The appearance of a human face rigorously changes with respect to age that makes Age Classification as a more challenging task. The algorithms such as, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Radial Basis Function (RBF), motivated many Face Researchers to focus their attention in classifying the human age into various age groups. The Classification rate produced by these existing algorithms is not significant indeed. In this study, Gaussian Mixture Models (GMM) is used for classifying the facial images into different age groups. A combination of Discrete Wavelet Transformation (DWT) and Sammon Map are used to extract the facial features. The performance of this approach is tested using Album-2 of MORPH database. A maximum classification rate of 99.52% is achieved in stage-1, whereas 99.46% is achieved in stage-2 using GMM. Also the accuracy achieved using Gaussian Mixture Model, is comparatively greater than K-NN.

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Section: Previous Workmentioning

confidence: 99%