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Face recognition with Gabor phase
Venkata, Anjaneya Subha Chaitanya Konduri
Venkata, Anjaneya Subha Chaitanya Konduri
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2009-07
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Electronic dissertations
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Abstract
Face recognition is an attractive biometric measure due to its capacity to recognize
individuals without their cooperation. This thesis proposes a method to dynamically recognize a
facial image with the help of its valid features. To validate a set of feature points, the skin portion
of the facial image is identified by processing each pixel value. Gabor phase samples are
examined, depending on whether they are positive or negative at each filter output, and feature
vectors are formed with positive or negative ones along with the spatial coordinates at the
validated feature points. The collection of feature vectors is referred to as the feature vector set.
The face recognition system has two phases: training and recognition. During the
training phase, all images from the database are automatically loaded into the system, and their
feature vector set is determined. When the test image arrives at the system, the feature vector set
of the test image is compared with that of database images. Feature vectors are location-specific,
and thereby similarities between the feature vectors of the test image and database images are
calculated, provided that they are from the same spatial coordinates. Once spatial coordinates are
matched by using exclusive-OR (X-OR) operation, the similarity is calculated from the values of
the feature vector. Simulations using the proposed scheme have shown that precise recognition
can be achieved.
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Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
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Wichita State University