  We propose a deep learning-based feature fusion approach for facial computing
including face recognition as well as gender, race and age detection. Instead
of training a single classifier on face images to classify them based on the
features of the person whose face appears in the image, we first train four
different classifiers for classifying face images based on race, age, gender
and identification (ID). Multi-task features are then extracted from the
trained models and cross-task-feature training is conducted which shows the
value of fusing multimodal features extracted from multi-tasks. We have found
that features trained for one task can be used for other related tasks. More
interestingly, the features trained for a task with more classes (e.g. ID) and
then used in another task with fewer classes (e.g. race) outperforms the
features trained for the other task itself. The final feature fusion is
performed by combining the four types of features extracted from the images by
the four classifiers. The feature fusion approach improves the classifications
accuracy by a 7.2%, 20.1%, 22.2%, 21.8% margin, respectively, for ID, age, race
and gender recognition, over the results of single classifiers trained only on
their individual features. The proposed method can be applied to applications
in which different types of data or features can be extracted.
