Dynamic multi-attribute priority based face attribute detection for robust face image retrieval system

• Published in: Multimedia tools and applications 2020-09, Vol.79 (33-34), p.24825-24849
• Main Authors: Suchitra, S., Poovaraghan, R. J.
• Format: Article
• Language: English
• Subjects: Accuracy; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Digital imaging; Digital media; Discrimination; Face recognition; Illumination; Image management; Image retrieval; Multimedia Information Systems; Special Purpose and Application-Based Systems; Websites
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09219-4

The increase in the popularity of social websites and smartphones has helped people to easily click photos and upload them on the internet. Almost 60% of the photos are human faces. There might be an increase in face search in future because human faces are closely associated with social media where people show special interest on specific personalities. The main issue raised in the face retrieval system is the various intra-class variances like expression, pose and illumination. Most of the conventional approaches lack the accuracy to meet the human intuition for retrieving images. The proposed approach develops a Dynamic Multi-Attribute Priority-based Face Attribute Detection (DMAP-FAD) method based on contextual information of the face (Race and Gender) by detecting the attributes dynamically. This approach can provide better discrimination of the face image retrieval system by detecting the attributes dynamically. The digital binning technique is applied for improving the lighting differences for illumination. The proposed method effectively minimized the semantic gap and achieved high accuracy by focusing on the variations involved in the pose, illumination, and expression with dynamically detected an optimal set of attributes from the original set of attributes by using contextual relationship. In the experimental results, the proposed method has been tested efficiently by the metrics such as F-Score, Precision and Recall with help of Pubfig and LFW datasets and it is observed that the proposed method obtained an overall accuracy of 95.63% for higher discrimination of face image retrieval system. The results are comparable with those of the state-of-the-art methods.