# Design and Implementation of a Multi-Modal Biometric System for Company Access Control +
## 1 Introduction  
Access control is a fundamental issue of any security system, and it is mainly devoted to checking the truthfulness of users' claimed identity, in order to both verify personal access rights and support tracing and logging services. Access control implies authorization, and it strongly relies on identity analysis and authentication. Within a company structure, the requirements and rules that preside over the access control may differ with respect to different locations, expected actions, and functions of principals. Moreover, they can vary over time. The main focus of this paper is authentication (i.e., the process of confirming the identity of an entity), and its deployment in a real context within a company security system where it should be responsible for regulating the main access and the transit to the different zones inside the company itself. Authentication involves the verification of the validity of at least one form of identification (e.g., documents, digital certificate, biometric identifiers, etc.). The most interesting approach foridentity assessment falls into the category \"checking something that the user is or does\" as the factors of authentication, known as inherence factors. Examples of inherence factors are static or dynamic biometric parameters like face, fingerprint, iris, retinal pattern, signature, voice, etc. These biometric identifiers are distinctive and measurable characteristics which can be used to label and describe individuals in an almost unique way [1]. There are several advantages in using biometrics: they cannot be lost or forgotten, and they require the person under recognition to be present at the check point. Additionally, it is difficult to forge them. Each biometric has its strengths and weaknesses, and the choice depends on the application. No single biometric is expected to effectively meet the security requirements of all the applications, thus each biometric technique is admissible and there is no \"optimal\" biometric characteristic [2]. The match between a specific biometric and an application is determined depending upon the operational mode of the application and the properties of the chosen biometric itself. Biometrics can be either singularly or simultaneously used for identification. In [3], a system that couples One-Time-Password technique with face recognition has been proposed. As there is no proof of correlation among the different biometric parameters of an individual, the combination of independent sources of information is very promising for the improvement of the quality of the recognition process. Multi-biometric systems are biometric systems that consolidate multiple sources of biometric evidence; the integration of evidence is known as fusion, and there are various levels of fusion, which can be divided into two broad categories: pre-classification (fusion before matching) and post-classification (fusion after matching) [4; 5]. Depending on the nature of the sources, multi-biometric systems can also be classified into different categories; multi-sensor systems, multi-algorithm systems, multi-instance systems, multi-sample systems, multi-modal systems, and hybrid systems [6]. Multi-modal biometric systems are more reliable than uni-modal systems due to the presence of multiple and independent pieces of evidence [7]. They address the problem of non-universality, since multiple traits ensure sufficient population coverage and provide anti-spoofing measures by making it difficult for an intruder to simultaneously spoof different biometric traits of a legitimate user. A system can operate either in selection or checking mode. The former is an identification process: a person's traits are acquired to establish the identity of the individual; it is a one-to-many search process in which the biometric characteristics set against all the database entries that represent enrolled users, in order to find those biometric references with a specific degree of similarity.