  This paper designs a high-performance deep convolutional network (DeepID2+)
for face recognition. It is learned with the identification-verification
supervisory signal. By increasing the dimension of hidden representations and
adding supervision to early convolutional layers, DeepID2+ achieves new
state-of-the-art on LFW and YouTube Faces benchmarks. Through empirical
studies, we have discovered three properties of its deep neural activations
critical for the high performance: sparsity, selectiveness and robustness. (1)
It is observed that neural activations are moderately sparse. Moderate sparsity
maximizes the discriminative power of the deep net as well as the distance
between images. It is surprising that DeepID2+ still can achieve high
recognition accuracy even after the neural responses are binarized. (2) Its
neurons in higher layers are highly selective to identities and
identity-related attributes. We can identify different subsets of neurons which
are either constantly excited or inhibited when different identities or
attributes are present. Although DeepID2+ is not taught to distinguish
attributes during training, it has implicitly learned such high-level concepts.
(3) It is much more robust to occlusions, although occlusion patterns are not
included in the training set.
