**1. Introduction**

Due to the rapid development in information technology, it has become possible to utilize biometrics for identifying and verifying persons [1–3]. Personal verification involves associating an identity with a specific individual. Verification or authentication of an identity is related to the authorization or refusal of an individual's personal identification, which is verified and confirmed side-by-side with the identity provided. This procedure is crucial for identifying a search query of a person.

In recent times, biometric verification and identification systems have gained popularity, which brought about their extensive usage [4]. Most significantly, it is common to see laptops with fingerprint readers, as well as a Windows 10 "hello" function, which both support biometric identification and verification [3,5–7]. The latter feature is available to users who sign up for biometry usage. Biometry saves the user the stress of regular logins into gadgets and wares (e.g., keys or cards) with privacy and identity theft challenges [8]. For instance, lost or forgotten login details can be

accessed by a third party and be used illegally. Biometry eliminates such problems, and greatly reduces the risk of copying or falsifying them. Nevertheless, biometry is not a complete and perfect solution for user verification and identification. Gathering of an individual's biometric data to create the person's biometric template is a complex process, which can sometimes yield an indefinite outcome [9]. However, the probability of success with biometric systems may vary up to 99% in the best systems. As such, biometric systems are greatly beneficial in curbing security challenges such as privacy invasion and identity theft problems (the most common being attacks carried out using dummies or models of the given body part).

Hand recognition remains the earliest form of available biometric characteristics used to identify and differentiate humans, as well as verify their identities [10]. Using the hand, it is possible to recognize, for example, hand geometry, fingerprints, palm lines, lines on finger joints, and image of the bloodstream. [11]. As soon as a person attains adulthood, features of the individual's hands remain the same for the rest of his or her life. Hence, these characteristics can be used to identify and/or verify the person [12]. Moreover, these characteristics can be scanned (excluding the fingerprint) using a camera with a basic resolution (640 x 480 pixels), and saved in biometric memory, unlike the scanning of the eye retina [13] (which may not necessarily be saved) for future references. From the foregone, we can say that it is safe and inexpensive to embark on a project of creating small devices for the scanning biometric characteristics of the hand.

A number of biometric systems currently exist that work based on the principle of the shape and surface elevation of the hand (jointly referred to as "hand contour" in the context of this work), and serves the purpose of a person's identity features [14–20]. The design and implementation of such systems offer several benefits, especially given that hardware and software requirements are very easy to come by. However, biometric characteristics of a hand contour is often not sufficient to distinguish individuals. Consequently, identification security is compromised, which leads to a high false-match rate (FMR). As a result, optimized biometric systems are continuously designed based on the biometric characteristics that are hidden, and cannot be replicated. For example, ultramodern finger print scanning results cannot be replicated, but the cost associated with its design and implementation is very high. A second possibility for improving identification and verification by biometric systems is the use of two or more biometric characteristics within a system. For instance, joint scanning of hand and blood stream offer better identification results, which, in recent times, have become a lot cheaper, and have improved their security [21]. This is enabled by the fact that false match rates (FMR) of the individual biometric characteristics are multiplied, which, in turn, decreases FMR. Hence, the whole system becomes more secure.
