Abstract:The small sample issue is a common problem in face recognition system, and multi-modal model has strong generalization ability to solve the problem of small sample, which has already become the most important area of research in pattern recognition, however, the low accuracy and efficiency of the model has become a major challenge. Based on this, this paper proposes a efficient multimode biometric face and fingerprint recognition system based on neural network, which provides more efficient identification though choosing a good feature extraction and recognition algorithms. The Adoption of biometric recognition to authenticate a person's identity has greatly improved operational efficiency and the recognition accuracy in comparison with adoption of password or passphrase. The feasibility and effectiveness of the method in this paper has been verified in multimode biometric system database.

The community structure is one of the most universal and important attributes of complex network, and the community division research of complex network is to reasonably divide the community structure practically existed in the complex network. Apply the thought and theoretical method of supernetwork into the community division research of complex network. Aiming at the deficiencies of present GN algorithm, combining theoretical method of standardization centricity from the perspective of supernetwork, a kind of new community division algorithm of complex network is constructed, and the new algorithm is verified and analyzed through the example. The experimental result reveals that the new algorithm is improved and perfected to some extent in the division result compared with GN algorithm. Algorithm Research GN algorithm is a kind of splitting algorithm. Compared with other methods, GN algorithm can be called as a kind of classical community division algorithm, it often can get comparatively good result after matching with the modularity of Q , and the limiting conditions of GN algorithm are few with high adaptability and wide application. However, after research, recently it reveals that