Advances in Face Image Analysis:: Theory and applications by

Language: eng
Format: azw3
ISBN: 9781681081113
Publisher: Bentham Science Publishers
Published: 2016-02-29T16:00:00+00:00

Fig. (8))

Radial curves for face with various face expression [33]. (30)

where cs, g, i, j are discrete curvelet coefficients, s, g and (i, j) denote scale, sub-band and position of face pixels respectively. After applying curvelet transform, by applying a two-stage thresholding algorithm salient feature points are extracted. To match feature points, a feature representation based on local information curvelet coefficients are defined. To this end, 5×5 patches centered on feature points, are used to employ local information based on curvelet coefficients. Then the feature vector are scaled by using a Gaussian weighting function. Additionally, to add the rotation invariance property to extracted features, the feature vectors are rearranged based on their orientation. When the features and their descriptors are extracted, they are used for face recognition using 3D face matching algorithm.

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