Interest in face identification systems has increased significantly due to the emergence of significant commercial opportunities in surveillance and security applications. In this paper, an approach is developed for c...Interest in face identification systems has increased significantly due to the emergence of significant commercial opportunities in surveillance and security applications. In this paper, an approach is developed for combining the MWT （multiwavelet transform） with a MWN （multiwavelet network） as face identification algorithm. Only quarter of the approximation of the multiwavelet of the face image will be used as input to the MWN where the approximation quarter of the resultant multiwavelet of the face image will be segmented into four parts. These parts will be treated as 3D representation of the face image and will be given to the MWN. This makes multiwavelets a well designed tool for face identification. The multiwavelet shows promise in combining the desirable feature of the face image. A fast procedure for computing the MWT is introduced. The algorithm developed in this paper are tested on a data base consisting of 480 face images. The proposed algorithm outperform the other methods where a 100% identification was achieved using the mentioned data base.展开更多
文摘Interest in face identification systems has increased significantly due to the emergence of significant commercial opportunities in surveillance and security applications. In this paper, an approach is developed for combining the MWT （multiwavelet transform） with a MWN （multiwavelet network） as face identification algorithm. Only quarter of the approximation of the multiwavelet of the face image will be used as input to the MWN where the approximation quarter of the resultant multiwavelet of the face image will be segmented into four parts. These parts will be treated as 3D representation of the face image and will be given to the MWN. This makes multiwavelets a well designed tool for face identification. The multiwavelet shows promise in combining the desirable feature of the face image. A fast procedure for computing the MWT is introduced. The algorithm developed in this paper are tested on a data base consisting of 480 face images. The proposed algorithm outperform the other methods where a 100% identification was achieved using the mentioned data base.
