Lecture-12 - Lecture-12 Face Recognition 1 Simple Approach...

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1 Lecture-12 Face Recognition
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2 Simple Approach Recognize faces (mug shots) using gray levels (appearance) Each image is mapped to a long vector of gray levels Several views of each person are collected in the model-base during training During recognition a vector corresponding to an unknown face is compared with all vectors in the model-base The face from model-base, which is closest to the unknown face is declared as a recognized face. Problems and Solution Problems : Dimensionality of each face vector will be very large (250,000 for a 512X512 image!) Raw gray levels are sensitive to noise, and lighting conditions. • Solution: Reduce dimensionality of face space by finding principal components (eigen vectors) to span the face space Only a few most significant eigen vectors can be used to represent a face, thus reducing the dimensionality
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