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Novel Method of Adult Age Classification Using Linear Wavelet Transforms


M. Chandra Mohan, V. Vijaya Kumar, V. Venkata Krishna


Vol. 10  No. 3  pp. 61-68


The present paper proposes an innovative technique that classifies adult images with age spans for every ten years based on the topological texture features in the facial skin. The present paper assumes that bone structural changes do not occur after the person is fully grown that is the geometric relationships of primary features do not vary. That is the reason secondary features are identified and exploited. The secondary features that are exploited in the present paper are Topological Texture Features (TTF) on two-level linear wavelet transform of the facial skin. Based on TTF’s, the present paper classified the age of an adult, into seven categories i.e in the age groups of 16 to 25, 26 to 35, 36 to 45, 46 to 55, 56 to 65, 66 to 75 and 76 to 85. The proposed method is rotation and pose invariant. The experimental evidence on FG-NET aging database and Google Images clearly indicates the high classification rate of the proposed method. The recognition rates between various linear wavelet transforms are compared.


Topological Texture Features, linear wavelet transforms, facial skin, Rotation invariant, Pose invariant