Abstract
Biometric recognition systems use certain human characteristics such as voice, facial features, fingerprint, iris or hand geometry to identify an individual or verify their identity. These systems have been developed individually for each of these biometric modalities until they achieve remarkable levels of performance. Biometrics is a measure of biological characteristics for the identification or authentication of an individual based on some of its characteristics. Although biometric recognition techniques promise to be very effective, At present, we can not guarantee an excellent identification rate based on a single biometric signature with unimodal biometric systems. Thus the error rates of unimodal biometric systems are relatively high due to all these practical problems, which makes them impractical for the use of critical safety applications. To resolve these problems, a solution is used in the same system in several biometric modalities, called a multimodal biometric system (MBS). MBSs combine different modalities in a unique recognition system. The multimodal fusion allows improving the results obtained by a single biometric characteristic and making the system more robust to noise and interference and more resistant to possible attacks. Fusion may be carried out at the level of signals acquired by the different sensors, of the parameters obtained for each modality, of the scores provided by unimodal experts or of the decision taken by said experts. In the case of fusion, the features obtained from the various biometric methods must be homogenized before the process of fusion is accomplished. This article describes the evolution of a multi-modal biometric identification system depends on 3 biometrics-face, iris & fingerprint. Feature extraction is done using the Gabor Wavelet method and classification is accomplished using the Random Forest classifier. This proposed method is applicable in real-life applications to identify biometric for offices, hospitals, and colleges/universities and so on.
Highlights
This infatuation leads to the growth of a wide variety of biometric methods: from the classical ones, such as the study of fingerprints [1] or iris [2], to more exotic ones like the recognition of the gait [3], recognition of the shape of the ear [4]
The proposed research work depends upon the work of Walia et al (2019) [21]. They presented a research paper on multimodal biometric identification depending upon score level fusion of iris, fingerprint & finger-vein images
This work presented the process of identifying individuals by multimodal biometrics
Summary
The identification of individuals corresponds to the search for the identity of the person who appears in a database. It can be used to authorize the use of services, for example, to control access to a highly secure area for which only a limited number of people (registered in a database) have access authorization, as it can be used to recognize criminals. To meet these needs, biometrics seems to be a practical, effective solution whose cost in effort and money is constantly decreasing. The acquisition and the processing can be done successively, one speaks of architecture in series, or simultaneously, one speaks of architecture in parallel
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