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BPSO based neural network approach for content-based face retrieval

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Abstract

This study presents Biogeography Particle Swarm Optimization with Neural Network, which is based on Content-Based Image Retrieval and is used to solve face-related problems during face matching. A new approach is deployed called Content-Based Face Retrieval. In Content-Based Face Retrieval, face images are retrieved based on low-level features like colour and texture. A new learning method of migration of biogeographic swarm intelligence, based on Particle Swarm Optimization to cut off the local optima has been introduced. Biogeography Particle Swarm Optimization is easy to set the neural network’s weights and threshold. Biogeography Particle Swarm Optimization based on Neural Network uses the fitness function as a comparison between query and database pictures. Usually, the similarity measure function depends on colour and texture features. A colour histogram is used to determine an image’s colour feature, and filters are reused to obtain texture features. The proposed method estimates the quality check and introduces tests on the FEI dataset.

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  1. Department of Computer Science & Engineering, Uttrakhand Technical University, Dehradun, 248007, India

    Deepika Dubey

  2. Department of Electronic and Communication Engineering, Rajkiya Engineering College, Sonbhadra, 231206, India

    Geetam Singh Tomar

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Dubey, D., Tomar, G.S. BPSO based neural network approach for content-based face retrieval. Multimed Tools Appl 81, 41271–41293 (2022). https://doi.org/10.1007/s11042-022-13208-0

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