Human age estimation using enhanced bio-inspired features (EBIF)

The Aging process is a non-reversible process, causing human face characteristics to change over time as hair whitening, muscles drop and wrinkles. Recently, age estimation from facial images has emerged as a prominent research area. One of the most successful works is based on biologically inspired features (BIF). In this paper we extend BIF by incorporating fine detailed facial features, automatic initialization using active shape models and analyzing a more complete facial area by including the forehead details. Furthermore, we combine regression-based and classification-based models and test them experimentally on standard datasets showing the superiority of our proposed algorithm (extended BIF - EBIF) over the state-of-the-art methods.