Abstract
Manifold learning is an effective dimension reduction method to extract nonlinear structures from high dimensional data. Recently, manifold learning started to attract attention within the research communities of image analysis, computer vision, and document data analysis. In this paper, we propose a Manifolded AdaBoost algorithm towards automatic 2D face recognition by using AdaBoost to fold the manifold space dimension and exploit the strength of both techniques. Experimental results support that the proposed algorithm improve over existing benchmarks in terms of stability and recognition precision rates.
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Lu, C., Jiang, J., Feng, G., Qing, C. (2008). A Manifolded AdaBoost for Face Recognition. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85563-7_21
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DOI: https://doi.org/10.1007/978-3-540-85563-7_21
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