PCA Based Human Face Recognition with Improved Methods for Distorted Images due to Illumination and Color Background

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)277-283
Journal / PublicationIAENG International Journal of Computer Science
Issue number3
Publication statusPublished - 2016


Various illumination invariant techniques are being examined in order to identify the one which works well with principle component analysis for human face recognition. Experimental results show that by applying the technique called Gradientfaces at the pre-processing stage which computes the orientation of the image gradients in each pixel of the face images and uses the computed face representation as an illumination invariant version of the input image, it can greatly improve the recognition rates. From a low recognition rate of 6.25% up to 60.75% testing on the Asian face database which has images with various illumination and from a recognition rate of 38% up to 99.5% testing on the Faces94 face database which has color images with slightly darker faces and green background.

Research Area(s)

  • Face recognition, Gradientfaces, Illumination insensitive measure, Principle component analysis (PCA)