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A novel approach for face recognition using biogeography based optimization with extinction and evolution

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Abstract

Evolutionary algorithms are one of the most emerging fields in pattern recognition and many computationally complex problems can be solved using evolutionary algorithms as heuristics. Face recognition is one of the most studied research topics all due to its very vast application in almost every field of the present hour but there is no such algorithm present which almost give 100% accuracy on different datasets. A variant of Biogeography Based Optimization (BBO) which includes the features of evolution, extinction and changes in the methodology of migration (immigration and emigration) have been applied to solve the problem of face recognition. Evolutionary algorithms iteratively try to improve the candidate solutions for a given fitness function and BBO is one such algorithm. This inclusion of extra features improves the performance of BBO significantly. The most important task in face recognition is feature extraction which helps to differentiate between the faces. A combination of PCA (Principal Component Analysis) for feature extraction and SVM (Support Vector Machine) for classification. The proposed variant of BBO is applied with the vectors as the candidate solution and an optimal set of eigenfaces are obtained which are then used to project the points to a new feature space where the interclass distance is minimal at the same time intraclass distance is maximal. The test images are then classified in this feature space. On testing our algorithm for 5 face different datasets namely Extended Yale B, MUCT, Faces96, Georgia Tech and Grimace the accuracy obtained with such a large variety of datasets clearly shows the effectiveness of our proposed algorithm. Even though the datasets are varied in terms of size, length of images, brightness, ethnicity of subjects, expressions, focus on the facial parts the algorithm achieve 100% accuracy on Grimace and Extended Yale B and a near to 100% accuracy of 99% on MUCT and 99.50% on faces96. On Georgia Tech dataset, an accuracy of 97.34% has been achieved which is enough to prove a significant improvement from the previous used algorithms of face detection.

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Acknowledgements

The author would like to thank Anurag Jain, Chirag Dua and Kamal, undergraduate students at MNIT Jaipur for helping in the data collection work required for addressing the comments of the reviewers.

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  1. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India

    Lavika Goel

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Goel, L. A novel approach for face recognition using biogeography based optimization with extinction and evolution. Multimed Tools Appl 81, 10561–10588 (2022). https://doi.org/10.1007/s11042-022-12158-x

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