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Post on 05-Dec-2014
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- 1. Face Recognition Using View Morphing
- 2. Motivation View morphing has tremendously been used in special effects. It allows us to combine the salient features of different images into one. In surveillance and security systems, where cameras are used to capture the footage of people coming in and out. It is tough to capture the perfect frontal face of any person.
- 3. Motivation We often get faces from certain angle. In such scenarios it becomes very difficult to perform face recognition on all the faces seen in the footage and get the correct results.
- 4. Motivation Face recognition technology is rapidly growing as evidenced by the new surveillance system. Here is an example of surveillance system that can recognize a face from 36 million other faces in one second, from Hitachi. It processes faces as it records and keep a pre indexed gallery of faces rather than a messy footage. Super speed is achieved as it compares the faces in real time Source : Hitachi Face Recognition System in Surveillance Systems by pre grouping faces with http://mashable.com/2012/03/23/hitachi-face-recognition/ similar features, so that it can narrow down the field of search quickly.
- 5. Motivation Limitations The software assumes that people are looking right into the camera or facing not more than 30 degrees of the centre. Also it requires the people to be close to the camera. Anything smaller than 40 x 40 pixel square face is not recognizable at all.
- 6. Research Problem Can we use View Morphing in Recognition systems ? As we see, for any surveillance system we need a frontal face to perform recognition. We investigate through our study that whether we can generate a face given the footage images which can be used for face recognition.
- 7. Research Problem Primary focus of the research was to be able to perform view morphing on face images with different poses and expressions to generate intermediate pose and expression images which capture all the salient features of the face.
- 8. Datasets Used For pose variant images we used MULTI PIE database. Sample images from the database
- 9. Dataset Used For expression variant images we used JAFFE database. Sample images from the database
- 10. Introduction View morphing is a technique used for transforming one image into another. Hollywood films and Disney uses morphing for effects and animation. Algorithms has been used to generate morphing between images of face of different people as well as between images of face of individuals. Warping and Cross Fading are the two techniques involved in any kind of morphing.
- 11. Introduction Cross Fading is simple but does not produce smooth transitions, major problem is to warp. Cross Fading generate a series of frames that are linear combinations of the original images. Example of cross fading, Source : http://odonnell- wiki.cs.uchicago.edu/inde x.php/TenStepCrossfade
- 12. Introduction For smoother transitions it is necessary to use warping. Warping transforms shape where as cross dissolve transform color only color. We will now demonstrate the two different approaches that were taken during the project involving different kinds of warping and cross fade techniques.
- 13. Approach 1For Pose Variant Face Images
- 14. Feature Based Field Morphing Outline of the processInput Image 1 Input Image1& & image 2 image 2Pre Extracting Coordinate CrossProcessing Features Transform Dissolve
- 15. Feature Based Field Morphing Pre Processing Estimating Fundamental Matrix (F) using 8 Point Algorithm. Finding Eigen vectors of F , the epipoles. Rotate the epipolar vectors and compute the transform. Apply the transform to get the prewarped image.
- 16. Estimating F using 8-point algorithm To calculate F, the user first inputs corresponding feature points from both the images. These points are used to perform a mapping between the two images.
- 17. Estimating F using 8-point algorithm The fundamental matrix F is defined by x Fx 0 for any pair of matches x and x in two images. f11 f12 f13 Let x=(u,v,1)T and x=(u,v,1)T, F f 21 f 22 f 23 f 31 f 32 f 33 each match gives a linear equationuu f11 vu f12 u f13 uv f 21 vv f 22 v f 23 uf31 vf 32 f 33 0
- 18. 8-point algorithm f11 f12 f13u1u1 v1u1 u1 u1v1 v1v1 v1 u1 v1 1 f 21u 2 u 2 v2 u 2 u 2 u 2 v2 v2 v2 v2 u 2 v2 1 f 22 0 f 23u n u n vn u n u n u n vn vn vn vn u n vn 1 f 31 f 32 f 33 In reality, instead of solving Af 0, we seek f to minimize Af , least eigenvector of A A.
- 19. 8-point algorithm Problem? F should have rank 2 To enforce that F is of rank 2, F is replaced by F that minimizes F F subject to the rank constraint. This is achieved by SVD. Let F UV, where 1 0 0 1 0 0 0 2 0 , let 0 2 0 0 0 3 0 0 0 then F U V is the solution.
- 20. Image Transformation After Computing F we now have variables that can be used to transform the image and compute the pre-warped image. Following is an example from the experiment. The first two images are the original images with features marked for estimating F. The last two images are the resultant pre-warped images.
- 21. Feature Morphing Algorithm Step 1 Input corresponding feature LINES. For every line PQ in source image there is a matching line PQ in the destination image.
- 22. Feature Morphing Algorithm Step 2
- 23. Multiple Pair of Lines: Problem?
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