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

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.

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In  CVPR 2010

Publisher  IEEE Computer Society
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