Zhong Wu, Qifa Ke, Jian Sun, and Heung-Yeung Shum
State-of-the-art image retrieval systems achieve scala- bility by using bag-of-words representation and textual re- trieval methods, but their performance degrades quickly in the face image domain, mainly because they 1) produce vi- sual words with low discriminative power for face images, and 2) ignore the special properties of the faces. The lead- ing 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 re- trieval system. For this purpose, we develop a new scal- able face representation using both local and global fea- tures. In the indexing stage, we exploit special proper- ties 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 (40 bytes) to encode the dis- criminative global feature for each face. In the retrieval stage, candidate images are firstly retrieved from the in- verted index of visual words. We then use a new multi- reference distance to re-rank the candidate images using the hamming signature. On a one-millon face database, we show that our local features and global hamming signa- tures are complementary—the inverted index based on local features provides candidate images with good recall, while the multi-reference re-ranking with global hamming signa- ture leads to good precision. As a result, our system is not only scalable but also outperforms the linear scan retrieval system using the state-of-the-art face recognition feature in term of the quality.
|Published in||CVPR 2010|
|Publisher||IEEE Computer Society|
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Zhong Wu, Qifa Ke, Jian Sun, and Heung-Yeung Shum. Scalable Face Image Retrieval with Identity-Based Quantization and Multireference Reranking, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE, October 2011.