Age Classification Based on Simple Lbp Transitions

AbstractThe research related to age estimation using face images has become increasingly important, due to the fact that it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extraction and feature classification; both of which are important in order to improve the performance. The present paper derived “Transition based Fuzzy LBP” (TFLBP) method for the classification of facial image into five age groups as child (0-12), young adults (13-25), middle-aged adults (26-45), senior adults (46-60) and Senior citizens (> 60) .The proposed TFLBP used the concept of Fuzzy texture unit on LBP and derived two different LBP unit values with four pixels. The first one is from four-connected pixels of cross diagonal representation of Fuzzy LBP (FLBP) and the second one is from four-non connected corner pixels of FLBP. On this distinct FLBP the transitions are counted and age classification is derived from this. Age classification performance is compared with the various fuzzy based texture classification methods. The results demonstrate that superior performance is achieved by the proposed method.