This paper presents a novel fusion strategy for personal identification using face and palmprint biometrics. In the context of biometrics, three levels of information fusion schemes have been suggested: feature extraction level, matching score level and decision level. This work considers the first level fusion scheme. The purpose of our paper is to investigate whether the integration of face and palmprint biometrics can achieve higher performance that may not be possible using a single biometric indicator alone. Both Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are considered in this feature vector fusion context. We compare the results of the combined biometrics with the results of the individual face and palmprint. It is found that the performance is significantly improved in both cases, especially in the case of feature fusion using ICA obtaining encouraging results with a 99.17% recognition accuracy rate using a test set sized of 40 people.
|Number of pages||7|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 1 Dec 2004|
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)