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

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.