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We have shown that by decomposing a face image using wavelet transform, the low-frequency face image is less sensitive to the facial expression variations.
And we consider the histogram-based feature as our final feature representation for each face image.
Visible light face image contains abundant texture information which is very useful for face recognition.
In this paper, we benefit from the daisy descriptor for face image representation in image matching.
Reliable face boxes output will be much helpful for further face image analysis.
Vetter, T. Synthesis of novel views from a single face image.
Since modular PCA processes non-overlapping regions of a face image to produce weight vectors, we design a parallel architecture where each parallel path has a processing element to process a predefined region of a face image.
different information value of various face image areas.
Each face image is first converted to a vector.
face symmetry; different information value of various face image areas.
indicates correctly classifying a face image as a face.
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