An individual identification system is developed. In this system, we unify profile curve identification and full face image identification to obtain more successful recognition rate. In profile cruve identification process, the P-type Fourier descriptor is made use of. In full face image identification process, mosaic density values are made use of. A combination of the two processes shows higher recognition rates than those obtained by each single process.

A method based on clustering is presented for restoring and segmenting gray scale images. An optimum clustering obtained by a gradient method gives an image with gray scale values which vary smoothly in each segmented region. The method is also applied to restoration from sparsely sampled data.