Similarity Guided Deep Face Image Retrieval
This work addresses the need for fast and accurate face image retrieval in large databases, offering an incremental improvement over existing deep hashing methods by better capturing complex similarities.
The paper tackles the problem of face image retrieval by proposing a Similarity Guided Hashing (SGH) method that incorporates self and pairwise-similarity to improve retrieval quality, achieving state-of-the-art performance on benchmarks and a new large-scale dataset.
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.