Age Classification from Facial Images System?

In Computer vision system, rapidly expanding various applications. The goal in this paper is to develop a designing age classification system from the characteristics and information that can extract from the human face images for both sexes. The system proposed new algorithm that merging two features techniques (local and global) features. The local features including (primary face features), so the global features including (secondary face features). The new method in this paper present (local binary pattern) as a new technique uses in wrinkle analysis , so as this method uses to classify the input face images into one of four age groups: Baby, young, young adult and senior, and eight age categories: [1-6, 7-11, 12-19, 20-29, 30-39, 40-49, 50-65,66++]. This method based on human face region which contains a lot of information and properties that describe the head growth and face aging pretenses. These information can be used by the human brain to estimate the face age dependent on the external features that shows the craniofacial changes in geometrical characterize results by the growth of the head that changes the primary face features locations, the primary face features are: the center of the two eyes, nose peak, mouth peak, top head, face sides and the chin point, from these primary features we compute the geometrical ratios that distinguish babies faces from the three age groups: young, young adult and senior. The other changes that appear when the face aging is the texture changes which are the secondary features can be used to estimate the age of the face. The secondary face features may be the wrinkle appearance, duple chin, and eye bags. The wrinkle lines are calculated in the curliest five regions these are: for head, under two eyes and cheeks regions. These lines are computed and used to distinguish young, young adult, and senior age groups and age categories.