AGE GROUP CLASSIFICATION USING HAAR FEATURES EXTRACTION AND KNN

Recognition of the one of the most facial varieties, for example, OCR(character), expression and gender has been broadly contemplated. Programmed age group and foreseeing future countenances have once in a while been investigated. With the progression in age of a human there occurs some changes in the face features. This paper worries with giving a procedure to gauge age gathering utilizing face features. This procedure includes three stages: Location, Feature Extraction and Classification. The geometric components of facial pictures like wrinkle topography, face edge, left eye to right eye separation, eye to nose separation, eye to jaw separation and eye to lip separation are computed. Taking into account the surface and shape data age grouping is done utilizing K-Means bunching calculation. Age reaches are ordered progressively relying upon number of gatherings utilizing K-Means bunching calculation. The acquired results were huge. This paper can be utilized for anticipating future confronts, arranging gender orientation, and expression recognition from facial image

Towards automatic face identification robust to ageing variation

A large number of high-performance automatic face recognition systems have been reported in the literature. Many of them are robust to within class appearance variation of subjects such as variation in expression, lighting of subjects such as variation in expression, lighting and pose. However, most of the face identification systems developed are sensitive to changes in the age of individuals. We present experimental results to prove that the performance of automatic face recognition systems depends on the age difference of subjects between the training and test images. We also demonstrate that automatic age simulation techniques can be used for designing face recognition systems, robust to ageing variation. In this context, the perceived age of the subjects in the training and test images is modified before the training and classification procedures, so that ageing variation is eliminated. Experimental results demonstrate that the performance of our face recognition system can be improved significantly, when this approach is adopted. © 2000 IEEE