  Generally, facial age variations affect gender classification accuracy
significantly, because facial shape and skin texture change as they grow old.
This requires re-examination on the gender classification system to consider
facial age information. In this paper, we propose Multi-expert Gender
Classification on Age Group (MGA), an end-to-end multi-task learning schemes of
age estimation and gender classification. First, two types of deep neural
networks are utilized; Convolutional Appearance Network (CAN) for facial
appearance feature and Deep Geometry Network (DGN) for facial geometric
feature. Then, CAN and DGN are integrated by the proposed model integration
strategy and fine-tuned in order to improve age and gender classification
accuracy. The facial images are categorized into one of three age groups
(young, adult and elder group) based on their estimated age, and the system
makes a gender prediction according to average fusion strategy of three gender
classification experts, which are trained to fit gender characteristics of each
age group. Rigorous experimental results conducted on the challenging databases
suggest that the proposed MGA outperforms several state-of-art researches with
smaller computational cost.
