From Deep Features to Swarm Intelligence: A Novel Methodology for Kinship Verification
Article No.: 60, Pages 1 - 4
Abstract
Abstract In the empire of biometric recognition, kinship verification plays a pivotal role, Main aim of this research is to discover if two individuals share a biological relationship based on facial features. The advent of deep learning has revolutionized this domain, with convolutional neural networks (CNNs) emerging as a potent tool for feature extraction. Specifically, the VGG16 architecture, renowned for its effectiveness in various image recognition tasks, has been repurposed for kinship verification. However, the sheer volume of features extracted through such deep networks often includes redundant or irrelevant information. This paper delves into an innovative approach of combining the prowess of VGG16 for feature extraction with the metaheuristic search capabilities of the Particle Swarm Optimization (PSO) algorithm for feature selection. This algorithm inspired by the collective behavior of swarms in nature, optimizes the feature set by iteratively refining the selection towards those that contribute most significantly to accurate kinship classification By harnessing PSO, we aim to reduce the feature dimensions by selecting only the most salient and relevant features, thereby optimizing the performance of kinship verification systems. Preliminary results underscore the potential of this synergistic combination, paving the way for more efficient and accurate kinship recognition.
References
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
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Association for Computing Machinery
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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