A Comparative Study of Linear Subspace Analysis Methods for Face Recognition
نویسندگان
چکیده
Face recognition is a typical problem of pattern recognition and machine learning. Among these approaches to the problem of face recognition, subspace analysis gives the most promising results, and becomes one of the most popular methods. This paper researches typical subspace analysis approaches, based on the introduction of main approaches of linear subspace analysis, such as Principal Component Analysis (PCA) , Linear Discriminant Analysis(LDA) and Fast Independent Component Analysis (FastICA), the application of these approaches for face recognition by ORL database and YALE B database are investigated, and the advantages and disadvantages are compared. Experimental results show that the LDA approach leads to better classification performance than PCA approach, while the FastICA approach leads to the best classification performance with the improvement of nearly 3% compared with the LDA approach h.
منابع مشابه
A comparison of subspace analysis for face recognition
We report the results of a comparative study on subspace analysis methods for face recognition. In particular, we have studied four different subspace representations and their ‘kernelized’ versions if available. They include both unsupervised methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA), and supervised methods such as Fisher Discriminant Analysis ...
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