Image Processing

Topics: Linear interpolation, Vector space, Linear algebra Pages: 6 (1677 words) Published: January 31, 2013


A study on face morphing is proposed.The algorithms explains the extra feature of points on face and based on these feature points, images are portioned and morphing is performed. The algorithms has been used to generate morphing between images of face of different people as well as between images of face of individuals. To do face morphing, feature points are usually specified manually in animation industries. However,this approach involved computation of 3N dimensional probability density function, N being the number of pixels of the image, and we thought the approach was too much computation-demanding.Within the scope of this project, we built up a prototypical automatic animation generator that can take an arbitrary pair of facial images and generate morphing between them.The results of both inter and intra personal morphing are subjectively satisfactory.

    Morphing applications are everywhere. Hollywood film makers use novel morphing technologies to generate special effects, and Disney uses morphing to speed up the production of cartoons. Among so many morphing applications, we are specifically interested in face morphing because we believe face morphing should have much more important applications than other classes of morphing. 2.Outline of Procedures adopted  fig 1

2.1 Pre-Processing
    When getting an image containing human faces, it is always better to do some pre-processing such like removing the noisy backgrounds, clipping to get a proper facial image, and scaling the image to a reasonable size.  So far we have been doing the pre-processing by hand because we would otherwise need to implement a face-finding algorithm.  Due to time-limitation, we did not study automatic face finder. 2.2FeatureFinding

    Our goal was to find 4 major feature points, namely the two eyes, and the two end-points of the mouth.  Within the scope of this project, we developed an eye-finding algorithm that successfully detect eyes at 84% rate.  2.2.1Eye-finding

    The fig 2 illustrates our eye-finding algorithm.  We assume that the eyes are more complicated than other parts of the face.  Therefore, we first compute the complexity map of the facial image by sliding a fixed-size frame and measuring the complexity within the frame in a "total variation" sense. Then, we multiply the complexity map by a weighting function that is set a priori.Afterwards, we find the three highest peaks in the weighted complexity map, and then we decide which two of the three peaks, which are our candidates of eyes, really correspond to the eyes.The similarity is measured in the correlation-coefficient sense. fig 2

    After finding the eyes, we can specify the mouth as the red-most region below the eyes.  The red-ness function is given by Redness = ( R > G * 1.2 ? ) * (R>Rth?) *  { R / (G + epsilon ) }  |

Where Rth is a threshold, and epsilon is a small number for avoiding division by zero.  Likewise,  we can define the green-ness and blue-ness functions.  The fig 3 illustrates our red-ness, green-ness, and blue-ness functions.  Note that the mouth has relatively high red-ness and low green-ness comparing to the surrounding skin.  Therefore, we believe that using simple segmentation or edge detection techniques we would be able to implement an algorithm to find the mouth and hence its end points.

2.3 Image Partitioning
    Our feature finder can give us the positions of the eyes and the ending points of the mouth, so we get 4 feature points. Beside these facial features, the edges of the face also need to be carefully considered in the morphing algorithm.  If the face edges do not match well in the morphing process, the morphed image will look strange on the face edges. We generate 6 more feature points around the face edge, which are the intersection points of the...
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