Multi-objective genetic programming for feature learning in face recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Bi:2021:ASC,

• author = "Ying Bi and Bing Xue and Mengjie Zhang",
• title = "Multi-objective genetic programming for feature learning in face recognition",
• journal = "Applied Soft Computing",
• year = "2021",
• volume = "103",
• pages = "107152",
• month = may,
• keywords = "genetic algorithms, genetic programming, Multi-objective optimisation, Evolutionary computation, Feature learning, Face recognition",
• ISSN = "1568-4946",
• URL = "https://yingbi92.github.io/homepage/2021/MOGP.pdf",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494621000752",
• DOI = "doi:10.1016/j.asoc.2021.107152",
• size = "14 pages",
• abstract = "Face recognition is a challenging task due to high variations of pose, expression, ageing, and illumination. As an effective approach to face recognition, feature learning can be formulated as a multi-objective optimisation task of maximising classification accuracy and minimising the number of learned features. However, most of the existing algorithms focus on improving classification accuracy without considering the number of learned features. In this paper, we propose new multi-objective genetic programming (GP) algorithms for feature learning in face recognition. To achieve effective face feature learning, a new individual representation is developed to allow GP to select informative regions from the input image, extract features using various descriptors, and combine the extracted features for classification. Then two new multi-objective genetic programming (GP) algorithms, one with the idea of non-dominated sorting (NSGPFL) and the other with the idea of Strength Pareto (SPGPFL), are proposed to simultaneously optimise these two objectives. NSGPFL and SPGPFL are compared with a single-objective GP for feature learning (GPFL), a single-objective GP for weighting two objectives (GPFLW), and a large number of baseline methods. The experimental results show the effectiveness of the NSGPFL and SPGPFL algorithms by achieving better or comparable classification performance and learning a small number of features.",
• notes = "MOGP.pdf is a 40 page preprint",

}

Genetic Programming entries for Ying Bi Bing Xue Mengjie Zhang

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