Hybrid Features Extraction for Adaptive Face Images Retrieval

Hybrid Features Extraction for Adaptive Face Images Retrieval

Adel Alti (LRSD Laboratory, Computer Science Department, Sciences Faculty, University Ferhat Abbas Setif-1, Setif, Algeria)
Copyright: © 2020 |Pages: 10
DOI: 10.4018/IJSE.2020010102

Abstract

Existing methods of face emotion recognition have been limited in performance in terms of recognition accuracy and execution time. It is highly important to use efficient techniques for improving this performance. In this article, the authors present an automatic facial image retrieval combining the advantages of color normalization by texture estimators with the gradient vector. Starting from a query face image, an efficient algorithm for human face by hybrid feature extraction provides very interesting results.
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Considerable works have been carried out on content-based image indexing. Existing works of content-based image retrieval using dominant colors as well as the complexity of their content. They use generic attributes such as color, shape or texture (Israel et al. 2004; Zhou et al. 2017;). Other systems use XML schemas to search for images on their semantic and visual content (Hong & Nah, 2004). These visual primitives can be categorized into three main types: color-based descriptors, texture-based descriptors and shape-based descriptors. Histograms (Boujemaa, Boughorbel, &Vertan, 2001) and Color Angles (Wang et al. 2010) are typical examples of the first type. In particular, color angles (Wang et al. 2010) are considered one of the most powerful discriminative algorithms that were applied to diverse classification problems including face recognition. In fact, color angles, are known to be particularly efficient to cope with high dimensional data spaces (Costa, Humpire-Mamani, & Traina, 2012). However, the problem with color angles is the fact of being frame-based classifiers i.e. they are inherently unable to model pixels dependencies.

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