Hair Consultant project
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This document discusses developing a hair consultant application. It proposes using computer vision techniques like Viola-Jones face detection and modeling faces as densely connected graphs or mixtures of trees to extract features from a user's face photo. These features would then be used to search a database and recommend suitable hairstyles. The document outlines the Viola-Jones algorithm and explains concepts like Haar features, integral images, AdaBoost and cascading. It also compares modeling faces as densely connected graphs versus mixtures of trees. The goal is to build a demo that can detect a user's face, analyze features, and suggest hairstyles tailored to their facial structure.
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Hair Consultant project
- 1. Hair consultant 2015/1/13 MMAI term project: Hair Consultant 1
- 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 2
- 3. Intent • Recommend user some nice hair styles according to user’s eyes, nose, eyebrow, mouth, skin color, the contour of the shape of user’s 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 5
- 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 9
- 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. It’s 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 Kr¨uger, 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
Editor's Notes
- 積分圖，資料結構
- 級聯架構
- Local feature由gabor wavelet來表示
- 這裡的x向量其實就是一個pixel的位置(x, y) K => wave vector A jet J is defined as the set {Jj} of 40 complex coefficients obtained for one image point. 小j => 每一種kernel(共40種)
- only 3 frequencies and 4 orientations are represented in the figure 這是recognition的部分 非常簡單
- In practice， 用啟發式演算法(heuristic algorithm) to come close to the optimum within a reasonable time
- Database裡的model怎麼出來的 => 先手動打幾個model Assuming that two jets J and J’refer to object locations with small relative displacement d vector, Use known model as rigid model(landa =>ｉｎｆ) to find the most likely FBG coarse to fine approach 再來每一輪放寬landa來產生更具代表性的model
- The best scoring mixture m* specifies the estimated viewpoint, and the best scoring locations L* specify the estimated landmarks locations. we search over multiple scales on an image pyramid.
- Establishing the model M indicate the mixture, I indicate the image, L indicate the “configuration of parts” Phi => feature, Hog in our implement Shape的input沒有I => 只在乎mixture的合理性
- concatenate appearance weights and deformation weights together as the weight vector Chow-liu algorithm => information theorem(representing data as a tree with little information loss)