AGE CLASSIFICATIONS BASED ON SECOND ORDER IMAGE COMPRESSED AND FUZZY REDUCED GREY LEVEL (SICFRG) MODEL

摘要

One of the most fundamental issues in image classification and recognition are how to characterize images using derived features. Many texture classification and recognition problems in the literature usually require the computation on entire image set and with large range of gray level values in order to achieve efficient and precise classification and recognition. This leads to lot of complexity in evaluating feature parameters. To address this, the present paper derives a Second Order image Compressed and Fuzzy Reduced Grey level (SICFRG) model, which reduces the image dimension and grey level range without any loss of significant feature information. The present paper derives GLCM features on the proposed SICFRG model for efficient age classification that classifies facial image into a five groups. The SICFRG image mode of age classification is derived in three stages. In the first stage the 5 x 5 matrix is compressed into a 2 x 2 second order sub matrix without loosing any significant attributes, primitives, and any other local properties. In stage 2 Fuzzy logic is applied tPo reduce the Gray level range of compressed model of the image. In stage 3 GLCM is derived on SICFRG model of the image. The experimental evidence on FG-NET and Google aging database clearly indicates the high classification rate of the proposed method over the other methods.