Face Representation and Recognition based on Texture Scale and Orientation through Gabor Filter
Date Issued
2010
Date
2010
Author(s)
Liu, Kuan-Ting
Abstract
In the past ten years, face recognition has become a popular area in computer vision. This technique can be used in several applications, such as security system or photo categorization system. Although many technical papers and commercial systems have emerged, recognition of photos under uncontrolled environment is still a challenge. Here we will focus on recognizing different people in home photo datasets without any training procedure. Since Gabor filter has the multi-resolution and multi-orientation characteristics, we implement two algorithms, called Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and Histogram of Gabor Phase Patterns (HGPP), which use Gabor magnitude and Gabor phase as the face descriptor respectively. How to combine LGBPHS and HGPP is also addressed here. Moreover, we use multi-thread and GPU programming to reduce the computation time, and evaluate our approach on general face images from the FERET Database. Our approach can result in 96.71% precision in dividing into 109 clusters from 309 home photos, and 99.22% precision in dividing into 252 clusters from 838 home photos. On FERET Database, precision of our approach is 95.97%, which is higher than the previous research. In our implementation, the Gabor filter using GPU programming is more than 140 times faster than the single core version.
Subjects
Face Recognition
Gabor Filter
Histogram
Hierarchical Clustering
GPU
Type
thesis
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