Spider recognition by biometric web analysis
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Spider specie identification and verification based on pattern recognition of it cobweb
Expert Systems with Applications: An International Journal
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Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two commonly used feature extraction techniques. In this paper, a nonlinear Evolutionary Weighted Principal Component Analysis (EWPCA) based on Genetic Algorithms is proposed. Similar to LDA, the EWPCA maximizes the ratio of between-class variations to that of within-class variations, and achieves better classification performance than that of traditional PCA. Genetic Algorithms are chosen as the searching method to select optimal weights for the EWPCA. In face recognition, Evolutionary facial feature obtained by performing EWPCA is used as the representation of original face images. Experimental results on ORL and combo face databases prove that EWPCA outperforms both PCA, kernel PCA and LDA.