Compression of fingerprint images using hybrid image model
We present an efficient model-based fingerprint image compression scheme based on a hybrid image model. Our model is based on extracting ridge and valley contours and then reconstructing a hybrid surface by using the gray values on these contours. The hybrid model we utilized is the convex combination of the membrane and plate functionals used for surface reconstruction by regularization. Two parameters of this model is determined to obtain a good approximation of the original fingerprint image given the sparse data, on ridges and valleys. In this compression scheme, the ridge contours are coded efficiently by using a differential chain code, while the differences between consecutive gray values along the chains are encoded using Huffman coding. Also included in the compressed image are the mean value of each valley segment and two parameters of the hybrid model. One advantage of our approach as compared to transform based algorithms and wavelet-based algorithms is that features such as delta and core points, end points, bifurcation points can be extracted directly from compressed image even for very high values of the compression ratio. The algorithm has been applied to various fingerprint images, and high compression ratios like 45:1 have been obtained while keeping all the important features in the images.