1. Hair consultant 2015/1/13 MMAI term project: Hair Consultant
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2. Scenario Each time I walk into the hair salon, my hair
designer show me a bunch of photos of hair style to choose.
However, those guys on the magazine are always white and skinny.
They just look nothing like me! MMAI term project: Hair Consultant
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3. Intent Recommend user some nice hair styles according to
users eyes, nose, eyebrow, mouth, skin color, the contour of the
shape of users face FUN, mocking others. However, in a scientific
way. MMAI term project: Hair Consultant 3
4. roadmap MMAI term project: Hair Consultant 4 User Query
Extract Face Modeling Face Extract Feature Searching in DB
5. Face detection with Viola-Jones algorithm Using Viola-Jones
detection algorithm and a trained classification model for
detection. Robust Low False Positive rate Real time The goal is to
distinguish faces from non-faces MMAI term project: Hair Consultant
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6. Viola-Jones algorithm Haar features Integral image
Adaboost(face or not face) Cascading MMAI term project: Hair
Consultant 6
7. Haar feature -> integral image -> adaboost ->
cascading Feature of difference between sum of intensity of regions
Nose-like Haar feature Eyes-like Haar feature MMAI term project:
Hair Consultant 7
8. Haar feature -> integral image -> adaboost ->
cascading Data Structure !(DP) Shaded region sum = MMAI term
project: Hair Consultant 8
9. Haar feature -> integral image -> adaboost ->
cascading Go ask Hsuan-Tien Lin MMAI term project: Hair Consultant
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10. Haar feature -> integral image -> adaboost ->
cascading Goal: real time (but too many windows) Observe that on
average, only 0.01% of all sub-windows get positive response(face)
Cascading architecture MMAI term project: Hair Consultant 10
11. Modeling Face 2 choice Densely Connected Graph Mixture of
trees MMAI term project: Hair Consultant 11
12. Modeling by Densely Connected Graph MMAI term project: Hair
Consultant 12
13. Modeling by Densely Connected Graph Jet: The set of
convolution coefficients for kernels of different orientations and
frequencies at one image pixel is called a jet. MMAI term project:
Hair Consultant 13
14. Modeling by Densely Connected Graph MMAI term project: Hair
Consultant 14 Local feature measured by convolution with Gabor
wavelets 5 type of kernel with 8 orientation
15. Modeling by Densely Connected Graph Local feature by Gabor
wavelets MMAI term project: Hair Consultant 15
16. Modeling by Densely Connected Graph MMAI term project: Hair
Consultant 16 Face Bunch Graph
17. Modeling by Densely Connected Graph MMAI term project: Hair
Consultant 17 The scoring function: Taylor expension Displacement
estimated in the optimizing process
18. Modeling by Mixture of Trees mixture of trees with a shared
pool of parts V Highly efficient, much faster than densely
connected graph(FBG) Precise enough for our application MMAI term
project: Hair Consultant 18
19. Modeling by Mixture of Trees MMAI term project: Hair
Consultant 19 Mixture of trees ?
20. Modeling by Mixture of Trees MMAI term project: Hair
Consultant 20 Local feature(e.g.HOG)
21. Modeling by Mixture of Trees MMAI term project: Hair
Consultant 21 Best mixture
22. Modeling by Mixture of Trees Learning: Fully supervised
data Learn the trees using Chow-Liu tree algorithm (data
compression or inference) Learn the appearance and deformation
jointly using SVM MMAI term project: Hair Consultant 22
23. Its demo time ! MMAI term project: Hair Consultant 23
24. Q&A MMAI term project: Hair Consultant 24
25. reference
http://www.mathworks.com/help/vision/examples/face-detection-and-tracking-
using-the-klt-algorithm.html Face Recognition by Elastic Bunch
Graph Matching(1999), Laurenz Wiskott1, Jean- Marc Fellous,Norbert
Kruger, and Christoph von der Malsburg Face Detection, Pose
Estimation, and Landmark Localization in the Wild(2012), Xiangxin
Zhu, Deva Ramanan MMAI term project: Hair Consultant 25
