Abstract
Multimodal identification, which exploits biometric information from more than one biometric modality, is more secure and reliable than unimodal identification. Face recognition and fingerprint recognition have received a lot of attention in recent years for their unique advantages. However, how to integrate these two modalities and develop an effective multimodal identification system are still challenging problems. Hetero-associative memory (HAM) models store some patterns that can be reliably retrieved from other patterns in a robust way. Therefore, in this paper, face and fingerprint biometric features are integrated by the use of a hetero-associative memory method for multimodal identification. The proposed multimodal identification system can integrate face and fingerprint biometric features at feature level when the system converges to the state of asymptotic stability. In experiment 1, the predicted fingerprint by inputting an authorized user’s face is compared with the real fingerprint, and the matching rate of each group is higher than the given threshold. In experiment 2 and experiment 3, the predicted fingerprint by inputting the face of an unauthorized user and the stealing authorized user’s face is compared with its real fingerprint input, respectively, and the matching rate of each group is lower than the given threshold. The experimental results prove the feasibility of the proposed multimodal identification system.
Highlights
We propose a multimodal identification system based on fingerprint and face images by the Hetero-associative memory (HAM)
The face images come from ORL Faces Database and the fingerprint images come from CASIA-FingerprintV5 Database
To solve the multimodal identification problem based on face and fingerprint images, in this paper, we proposed a new feature fusion method for multimodal identification based on the HAM model, which can well fuse face features and fingerprint features of the authorized users
Summary
One of the weaknesses of these methods is that unauthorized persons can fabricate or steal protected data and make use of the rights of authorized users to engage in illegal activities. Though these traditional identification technologies, which usually face various threats in real world, are still playing an indispensable role on various occasions with a low request of security for their convenience and low cost, increasingly more consumers and enterprises choose to use biometric identification in numerous fields. Biometric identification technologies such as face recognition [1,2,3,4], fingerprint recognition [5,6,7], and gait recognition [8,9,10] are more secure and convenient than traditional technologies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
