Active Contour Model Applied In Face Detection

Recently, Digital image detection and segmentation have been playing an increasingly important role in image Processing. Due to both the tremendous variability of object shapes and the variation in image quality, when applying classical image detection and segmentation techniques such as edge detection and threshold etc, these techniques will be either failed completely or required some kind of post-processing step to remove invalid object boundaries in the detection and segmentation result. Active contour model is a new method which can achieve the object boundaries with the energy minimum function and have been paid attention extensively at home and overseas. This dissertation studied the active contour models at large and used them to extract object boundaries in face detection, which is the base of face recognitions.At first, some common methods of image pretreatment and edge detection were introduced in this dissertation, and then their strongpoint and shortcoming were compared. After discussing the parametric active contour model theory thoroughly, a reality-based interpretation of elasticity theory was given. Then the active contour models were recommended in image detection. The active contour is poor to converge on concave boundary and capture a narrow range, which are difficult to locate the object. Several exterior force fields of the active contour models were studied, which convergent features were compared then a new improved method was presented. This model includes tow stages. The first we made use of distance snake near to the object boundaries, while in the second the NGVF would drive the contour into the concave region. The finite difference method is applied to do numerical implementations of the new model. The experimental results showed the new model has a large capture range, can move a snake into the boundary concaves and is able to obtain the interested object contour precisely.Then there is a conflict among noise filtration and precise location when use the gradient vector flow active contour model to detect the edges. To filtrate the noise and get an excellent method, this thesis integrates cubic B-spline wavelet and GVF snake. First, we filtrated the image noise with large scale, then drive the GVF snake to an aimed contour that noise filtration well but location bad. Second, reducing the scale, and take the former contour as the GVF snake to drive.At last, this thesis uses the new improved active contour model to detect the face and tip contour. This method different from traditional detection methods, and have advantages in noise filtration and edge connection. The whole experiments were implemented using MATLAB code, and some results of object boundaries in image detection and segmentation are prominent. That indicated, when object boundaries were continuous, this new algorithm could cut down the sensitivity of initialized location and enhance the efficiency of convergent speed. Meanwhile the new algorithm would be applied to the real image and the effect is well.