A novel fingerprint recognition method based on a Siamese neural network

Abstract

Fingerprint recognition is the most widely used identification method at present. However, it still falls short in terms of cross-platform and algorithmic complexity, which exerts a certain effect on the migration of fingerprint data and the development of the system. The conventional image recognition methods require offline standard databases constructed in advance for image access efficiency. The database can provide a pre-processed image via a specific method that probably is compatible merely with the specific recognition algorithm. Then, the specific recognition algorithm starts the process of retrieving these specific pre-proessing images for recognition and inevitably will be blocked from other datasets. The proposed method in this research designed an embedded image processing algorithm based on a Siamese neural network in the recognition method that allows the proposed method to recognize images from any source without constructing a database for image storage in advance. In this research, the proposed method was applied to fingerprint recognition and evaluation of the proposed method was evaluated. The results showed that the accuracy of the proposed algorithm was up to 92%, and its F1 score was up to 0.87. Compared with the conventional fingerprint matching methods, its significant advantage in the FRR, FAR, and CR jointly indicated the remarkable correct recognition rate of the proposed method.

Security authentication method for power monitoring system operation and maintenance scenarios based on face recognition and fingerprint recognition

“…Through the above steps, the biometric information identification of the staff of power monitoring system operation and maintenance can be completed. By combining face recognition and fingerprint recognition technology, the biometric information of the staff is bound with the identification result, which provides help for the subsequent security authentication of operation and maintenance scenarios [7][8][9] .…”