Scalable Face Image Retrieval with Identity-Based Quantization and Multi-Reference Re-ranking

State-of-the-art image retrieval systems achieve scalability by using bag-of-words representation and textual retrieval methods, but their performance degrades quickly in the face image domain, mainly because they 1) produce visual words with low discriminative power for face images, and 2) ignore the special properties of the faces. The leading features for face recognition can achieve good retrieval performance, but these features are not suitable for inverted indexing as they are high-dimensional and global, thus not scalable in either computational or storage cost. In this paper we aim to build a scalable face image retrieval system. For this purpose, we develop a new scalable face representation using both local and global features. In the indexing stage, we exploit special properties of faces to design new component-based local features, which are subsequently quantized into visual words using a novel identity-based quantization scheme. We also use a very small hamming signature...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Zhong Wu, Qifa Ke, Jian Sun, Heung-Yeung Shum
• Keywords: Computer Vision, CVPR 2010, Face Image, Hamming Signature, Image Retrieval System