5 research outputs found
Face Recognition Using Discrete Cosine Transform for Global and Local Features
Face Recognition using Discrete Cosine Transform (DCT) for Local and Global
Features involves recognizing the corresponding face image from the database.
The face image obtained from the user is cropped such that only the frontal
face image is extracted, eliminating the background. The image is restricted to
a size of 128 x 128 pixels. All images in the database are gray level images.
DCT is applied to the entire image. This gives DCT coefficients, which are
global features. Local features such as eyes, nose and mouth are also extracted
and DCT is applied to these features. Depending upon the recognition rate
obtained for each feature, they are given weightage and then combined. Both
local and global features are used for comparison. By comparing the ranks for
global and local features, the false acceptance rate for DCT can be minimized.Comment: face recognition; biometrics; person identification; authentication;
discrete cosine transform; DCT; global local features; Proceedings of the
2011 International Conference on Recent Advancements in Electrical,
Electronics and Control Engineering (IConRAEeCE) IEEE Xplore: CFP1153R-ART;
ISBN: 978-1-4577-2149-