Age Group Estimation using Face Features
Ranjan Jana, Debaleena Datta, Rituparna Saha · 2013
Recognition of the most facial variations, such as identity, expression and gender has been extensively studied. Automatic age estimation and predicting future faces have 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. This process involves three stages: Pre-processing, Feature Extraction and Classification. The geometric features of facial images like wrinkle geography, face angle, left eye to right eye distance, eye to nose distance, eye to chin distance and eye to lip distance are calculated. 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.