Detection of the most facial variations, such as identity, expression and gender has been extensively studied. Automatic estimationm of age and predicting future faces are rarely been explored. With age progression of a human the face features changes. This paper concerns with providing a methodology to estimate age group using face features. Facial rejuvenation has driven a lot of research in the field of dermatology and plastic surgery, leading to many medical procedures This process involves three stages: Preprocessing, Feature Extraction and Classification.. Based on the texture and shape information age classification is done using K-Means clustering algorithm. Age ranges are classified dynamically depending on number of groups using K-Means clustering algorithm. The obtained results were significant. This paper can be used for predicting future faces, classifying gender, and expression detection from facial images. In this work a novel and computational fast algorithm is proposed for predicting age of humans with Peano Count Tree (P-Tree). The predicting system was developed and tested based on texture features extracted local gradient patterns (LGP) and gray level co-occurrence matrix (GLMC) to give better and more predicting accuracy with a range of time period.
feature extraction, Gradient Operator, GLCM, P-Classifier, bSQ, Peano Count Tree, Mining
