Human facial features extraction is a prime task for different computer vision problems especially human age estimation.
Age estimation is challenging due to age progression, extracting these features accurately are very important for better
performance. This paper extends our previous work on facial age estimation that is based on the biologically inspired
features (BIF). Two main technical contributions are presented: (1) we introduce a new set of Gabor features namely the
Imaginary and Magnitude parts along with the commonly used real part, and (2) we analyze different facial regions using
the Imaginary and Magnitude parts of Gabor features. The proposed features are tested on standard datasets showing
their superiority over the state-of-the-art methods.