  Face image retrieval, which searches for images of the same identity from the
query input face image, is drawing more attention as the size of the image
database increases rapidly. In order to conduct fast and accurate retrieval, a
compact hash code-based methods have been proposed, and recently, deep face
image hashing methods with supervised classification training have shown
outstanding performance. However, classification-based scheme has a
disadvantage in that it cannot reveal complex similarities between face images
into the hash code learning. In this paper, we attempt to improve the face
image retrieval quality by proposing a Similarity Guided Hashing (SGH) method,
which gently considers self and pairwise-similarity simultaneously. SGH employs
various data augmentations designed to explore elaborate similarities between
face images, solving both intra and inter identity-wise difficulties. Extensive
experimental results on the protocols with existing benchmarks and an
additionally proposed large scale higher resolution face image dataset
demonstrate that our SGH delivers state-of-the-art retrieval performance.
