Fusion Face and Palmprint for Human Recognition via Spectral Eigenvector | ||
| Engineering and Technology Journal | ||
| Article 1, Volume 27, Issue 4, March 2009, Pages 787-798 PDF (383.42 K) | ||
| DOI: 10.30684/etj.27.4.15 | ||
| Author | ||
| Hana M. Salman | ||
| Abstract | ||
| The Biometrics recognition systems act as an efficient method with broad applications in the area of: security access control, personal identification to humancomputer communication. From other hand, some biometrics have only little variation over the population, have large intra-variability over time, or/and are not present in all the population. To fill these gaps, a use of multimodal biometrics is a first choice solution [1]. This paper describes a multibiometrics method for human recognition based on new teacher vector identified as spectrum eigenface, and spectrum eigenpalm. The proposed combination scheme exploits parallel mode capabilities of the fusion feature vectors in matching level and invokes certain normalization techniques that increase its robustness to variations in geometry and illumination for face and palmprint. The correlation distance is used as a similarity measure. A threshold value is used to prevent the imposter for being recognized. Experimental results demonstrate the effectiveness of the new method compared to the unimodal biometrics for spectrum eigenface/eigenpalm. | ||
| Keywords | ||
| Modeling; ferrocement; corrosion; Service life; Durability; Metakaolin; Galvanized steel | ||
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