FACE RECOGNITION USING DISCRIMINATIVE APPROACH AND LOCAL BINARY PATTERN

Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. In this research project, we study the problem by designing and evaluating discriminative approaches. First, we find that the gradient orientation (GO), after discarding magnitude information, provides a simple but effective representation for this problem. When combined with a support vector machine (SVM). Our experiments are conducted on the Morph dataset and two large passport datasets, one of them being the largest ever reported for recognition tasks. Second, taking advantage of these datasets, we empirically study how age gaps and related issues (including image quality, spectacles, and facial hair) affect recognition algorithms. We found surprisingly that the added difficulty of verification produced by age gaps becomes saturated after the gap is larger than four years, for gaps of up to ten years. The face boundary can be differ from different age groups. Identify the age groups of a human based on the edges and stored in the database. Compare the input image and the existing database image. The input will be coming from any sensor devices like camera, robotics, GPS (global positioning system). Discriminative methods defines the input image appears in which clusters in the pattern space.