Table 2 Existing major score databases Database #Subjects Modality Quality BioSecure DS2 333 [20] Face, fingerprint and iris Yes BA-Fusion 295 [34] Face and speech No BANCA 52 [36] Face and speech No NIST-Multimodal 517 [37] Face and fingerprint No.
In the multi-modal research community, there are few multi-quality benchmark score databases; even Poh et al. [20] generated a score database with modality-specific quality measures (e.g., brightness and contrast for the face modality, with texture richness for fingerprint and iris modalities) and some general image quality measures, but this was a multiple sensor-based score database.
We have designed and implemented a human brain multi-modality database system with content-based image management, navigation and retrieval support for epilepsy.
The performances of all these fusion modules have been evaluated and compared with other approaches on a multi-modal database, containing both vocal and visual biometric modalities.
We performed two evaluations of the constructed score database, evaluations of each modality, and evaluations of the score-level fusion.
All the modalities of the proposed database are extracted using only a single sensor while the other databases mentioned above used multiple sensors.
We evaluate our method on the baseline ADNI-1 database with 807 subjects and three modalities (i.e., MRI, PET, and CSF).
The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa) and on a zoom-endoscopic image database using narrow band imaging.
Using libraries like Java dcm4che [ 26], the portal can automatically extract metadata contained within uploaded data files and incorporate them correctly in the database, creating associated events depending on image modality.
Additionally, the database considers one quality measure for the fingerprint modality (e.g., texture richness), and three quality measures for the iris modality (e.g., texture richness, difference between the iris and pupil diameters, and proportion of the iris used for matching).
