Abstract

In recent times, large-scale content-based face image retrieval has grown up with rapid improvement and it is an enabling technology for many emerging applications. Content based face image retrieval is done by computing the similarity between images in the databases and the input/query face image. Content based face image retrieval systems retrieves the image only using low level features therefore the retrieval rate is low in this system. To improve the retrieval rate sparse codeword based scalable face image retrieval system is developed. This system uses both low level features and high level human attributes. The proposed system has several stages to retrieve the images; 1. Low level features are extracted using LTP descriptor and utilize the automatically detected high level human attributes such as hair, Gender and race. 2. Sparse codeword techniques are applied on the low level features and attributes to generate the codeword. 3. The third stage is an indexing; in the indexing attribute embedded inverted indexing method is used. Using the methods mentioned above, face image retrieval system has achieved promising retrieval result. Experiment is conducted on different dataset such as pub fig, LFW and FERET. Among those dataset LFW dataset achieve higher performance.

Keywords:

Content based image retrieval, face image search, high level features, sparse coding,

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.