Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance

Abstract EN

The semantic-based facial image-retrieval system is concerned with the process of retrieving facial images based on the semantic information of query images and database images.

The image-retrieval systems discussed in the literature have some drawbacks that degrade the performance of facial image retrieval.

To reduce the drawbacks in the existing techniques, we propose an efficient semantic-based facial image-retrieval (SFIR) system using APSO and squared Euclidian distance (SED).

The proposed technique consists of three stages: feature extraction, optimization, and image retrieval.

Initially, the features are extracted from the database images.

Low-level features (shape, color, and texture) and high-level features (face, mouth, nose, left eye, and right eye) are the two features used in the feature-extraction process.

In the second stage, a semantic gap between these features is reduced by a well-known adaptive particle swarm optimization (APSO) technique.

Afterward, a squared Euclidian distance (SED) measure will be utilized to retrieve the face images that have less distance with the query image.

The proposed semantic-based facial image-retrieval (SFIR) system with APSO-SED will be implemented in working platform of MATLAB, and the performance will be analyzed.