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With benefits of fast query speed and low storage cost, hashing-based imageretrieval approaches have garnered considerable attention from the researchcommunity. In this paper, we propose a novel Error-Corrected Deep Cross ModalHashing (CMH-ECC) method which uses a bitmap specifying the presence of certainfacial attributes as an input query to retrieve relevant face images from thedatabase. In this architecture, we generate compact hash codes using anend-to-end deep learning module, which effectively captures the inherentrelationships between the face and attribute modality. We also integrate ourdeep learning module with forward error correction codes to further reduce thedistance between different modalities of the same subject. Specifically, theproperties of deep hashing and forward error correction codes are exploited todesign a cross modal hashing framework with high retrieval performance.Experimental results using two standard datasets with facial attributes-imagemodalities indicate that our CMH-ECC face image retrieval model outperformsmost of the current attribute-based face image retrieval approaches.

Using Deep Cross Modal Hashing and Error Correcting Codes for Improving the Efficiency of Attribute Guided Facial Image Retrieval