  Although deep learning has yielded impressive performance for face
recognition, many studies have shown that different networks learn different
feature maps: while some networks are more receptive to pose and illumination
others appear to capture more local information. Thus, in this work, we propose
a deep heterogeneous feature fusion network to exploit the complementary
information present in features generated by different deep convolutional
neural networks (DCNNs) for template-based face recognition, where a template
refers to a set of still face images or video frames from different sources
which introduces more blur, pose, illumination and other variations than
traditional face datasets. The proposed approach efficiently fuses the
discriminative information of different deep features by 1) jointly learning
the non-linear high-dimensional projection of the deep features and 2)
generating a more discriminative template representation which preserves the
inherent geometry of the deep features in the feature space. Experimental
results on the IARPA Janus Challenge Set 3 (Janus CS3) dataset demonstrate that
the proposed method can effectively improve the recognition performance. In
addition, we also present a series of covariate experiments on the face
verification task for in-depth qualitative evaluations for the proposed
approach.
