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Additional resources for Artificial Intelligence Perspectives and Applications: Proceedings of the 4th Computer Science On-line Conference 2015 (CSOC2015), Vol 1: Artificial Intelligence Perspectives and Applications
If ≤ | |, then go to Step 12, else then go to Step 20. Step 12. ℎ = 1, = 1. Step 13. If ℎ ≤ | |, then go to Step 14, else then go to Step 17. Step 14. If , = −1, then go to Step 15, else then go to Step 16. > , то = . Step 15. If Step 16. ℎ = ℎ + 1. Go to Step 13. Step 17. If < + 1, то = 1. Step 18. = + 1. 28 O. Dolinina and A. Shvarts Step 19. = + 1. Go to Step 11. Step 20. If = 0, then go to Step 21, else then go to Step 10. Step 21. For each : ( )= . Using calculated values of ( ∶ ∈ ) following sets are formed ( )= , = 1, … (5) with – total number of groups of defuzzyfied variables.
Acknowledgement. This research is funded by Vietnam National University of Ho Chi Minh City (VNU-HCMC) under the project ”Features descriptor under variation condition for real-time face recognition application”, 2014. References 1. : 2D and 3D face recognition: A survey. Pattern Recognition Letters 28(14), 1885–1906 (2007) 2. : Face recognition with local binary patterns. , Matas, J. ) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004) 3. : Face description with local binary patterns: Application to face recognition.
Gallery of pose ranges Preprocessing Pose estimation ... 15-25 degree Pre-prossed image ... Original image 40-60 degree Database 15 degree 25 degree 40 degree 60 degree ... Predicted pose range: 40-60 degree ... Chosen gallery ... 40 degree ... 60 degree Fig. 6. Choosing gallery for a proﬁle face image ... Pose Estimation Using Local Binary Patterns for Face Recognition LBP-based methods Pre-prossed image Matching 45 Id = 1 Feature vector Chosen gallery Feature set ... Id = 1 Id = 2 Id = n LBP-based Methods ...
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