Face recognition using improved local texture pattern Speaker: Wei-Lung Chang Date:2011/12/30 Authors: W.K. Yang and C.Y. Sun Source: 2011 9th World Congress on Intelligent Control and Automation (WCICA)

Outline Introduction Local binary patterns (LBP) Local ternary patterns (LTP) Their work Experiments Conclusion Comment 2

Introduction(1/2) Local texture patterns based methods have been widely used in face recognition. LBP and LTP are two typical feature descriptor methods. So they present an improved LBP method by replacing the central pixel with the average of the region, LTP too. 3

Introduction(2/2) The experimental results on ORL, AR face databases show that our present methods have better performance than LBP and LTP. A key issue in face recognition is to find effective descriptors for face appearance. There are two main approaches: holistic methods and local descriptor methods 4

Local binary patterns (LBP)(1/2)   5

Local binary patterns (LBP)(2/2) 6

Local ternary patterns (LTP)(1/3)   7

Local ternary patterns (LTP)(2/3)   8

Local ternary patterns (LTP)(3/3) For simplicity, each ternary patters is split into positive and negative part. They will be then combined in the final step of computation. 9

Their work (1/5)   10

Their work(2/5)   11

Their work(3/5)   12

Their work(4/5) As aforesaid, the face recognition algorithm can be described as follows: Step1. We calculate improved LBP and improved LTP on the image and get the code image. Step2. We divide the code images into m*n sub- regions. 13

Their work(5/5)   14

Experiments(1/4) They do experiments on ORL and AR databases to evaluate the performance of the improved methods. To evaluate the robustness of their proposed method against the noise, the add Gaussian noise on the ORL face database. 15

Experiments(2/4) 16

Experiments(3/4) 17

Experiments(4/4) The AR data based contains over 4000 color face image of 126 people. 18

Conclusion In this paper, they present an improve LBP and an improve LTP for face recognition. But it is difficult to set a suitable threshold in LTP. The experiments show that our present methods are more robust to the noise and illumination variations etc. than LBP and LTP. 19

Comment 這一篇提出的ILBP跟ILTP，為了減少雜訊的 影響，但在臉部變識的成功機率並沒有比原 來的方法還要好很多。 因為LBP跟LTP運用在人臉辨識上有十分快速 的優點，所以我想把它運用在我之後想做的 東西上面。 20