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

## TL;DR

This paper introduces a novel deep cross-modal hashing method combined with error correction codes to enhance the efficiency and accuracy of attribute-guided facial image retrieval.

## Contribution

The paper proposes a new deep cross-modal hashing framework integrated with error correction codes for improved facial image retrieval performance.

## Key findings

- Outperforms existing attribute-based face retrieval methods
- Achieves high retrieval accuracy on standard datasets
- Effectively captures relationships between face images and attributes

## Abstract

With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community. In this paper, we propose a novel Error-Corrected Deep Cross Modal Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain facial attributes as an input query to retrieve relevant face images from the database. In this architecture, we generate compact hash codes using an end-to-end deep learning module, which effectively captures the inherent relationships between the face and attribute modality. We also integrate our deep learning module with forward error correction codes to further reduce the distance between different modalities of the same subject. Specifically, the properties of deep hashing and forward error correction codes are exploited to design a cross modal hashing framework with high retrieval performance. Experimental results using two standard datasets with facial attributes-image modalities indicate that our CMH-ECC face image retrieval model outperforms most of the current attribute-based face image retrieval approaches.