Face Recognition with Local Binary Patterns and Partial Matching
Date Issued
2010
Date
2010
Author(s)
Shih, Pei-Ruu
Abstract
Due to the popularity of digital cameras, when people go on vacation, they will take many pictures. We think it is very meaningful and interesting to identify who are in these pictures. Therefore, different from traditional face recognition problem, we focus on those pictures taken by everyday people. These pictures may have different illumination, different poses, or partially occlusion, which will lead to significant performance dropping using traditional face recognition algorithm. Therefore, in this paper, we present a novel algorithm based on Local Binary Patterns and then combined with Partial Matching. In result evaluation, we will use the AR dataset, FERET dataset, and two home-photo datasets. In addition, we will compare with Google Picasa, which is almost the industry standard, and our performance is no worse than the performance of Google Picasa is using two home-photo datasets. In our system, we get the precision 99.46% in the home photo dataset I (309 images) with 100 clusters, and Picasa will get 99.92% precision with 94 clusters in web version and 100% precision with 99 clusters in download version. In addition, we will get the precision 99.59% in the home photo dataset II (838 images) from 253 images, and Picasa will get 99.49% precision with 190 clusters in web version and 100% precision with 253 clusters in download version. Moreover, we implement the system in a quad-core system, and also implement certain parts of our system in parallel. In our experiment, if we use only a single thread in our system, the executing time of 309 images is 73 minutes. However, if we use four threads in our quad-core PC, we can finish the same job in 24 minutes. It is almost three times faster than single-thread.
Subjects
Face recognition
Local Binary Patterns
Partial Matching
Multithreads
Type
thesis
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