Face Recognition Human–Machine Comparison Under Heavy Lighting
We demonstrate the performance of the Fisherface method for face recognition compared to human eye and simple Eigenface method. These methods do not involve many adjustable parameters. Images undergo the principal component analysis (PCA) and linear discriminant analysis (LDA). The goal of the work is a detailed comparison of the rates of false recognition between the computer vision methods and human perception. We find that humans show more flexibility and perform perfectly on easy tasks, whereas on tasks that are impossible to humans, Fisherface method also fails.
KeywordsFace Recognition Linear Discriminant Analysis Training Image Eigenvalue Equation False Recognition
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