A Structure Based Human Facial Age Estimation Framework under a Constrained Condition F in Python

Developing an automatic age estimation method toward human faces continues to possess an important role in computer vision and pattern recognition. Many studies regarding facial age estimation mainly focus on two aspects: facial aging feature extraction and classification/regression model learning. To set our work apart from existing age estimation approaches, we consider a different aspect-system structuring, which is under a constrained condition: given a fixed feature type and a fixed learning method, how to design a framework to improve the age estimation performance based on the constraint? We propose a four-stage fusion framework for facial age estimation. This framework starts from gender recognition, then goes to the second phase, gender-specific age grouping, followed by the third stage, age estimation within age groups, and finally ends at the fusion stage. In the experiment, three well-known benchmark datasets, MORPH-II, FG-NET, and CLAP2016, are adopted to validate the procedure. The experimental results show that the performance can be significantly improved by using our proposed framework and this framework also outperforms several state-of-the-art age estimation methods.