*4.1. Testing Methodology*

For the purpose of testing the biometric verification system proposed in this paper, 280 (40 subjects, where each provided seven images) images of the hands of different persons was used. Although this value is small compared to existing works by References [27,29–34,67], this was mainly due to the unavailability of the scanning system as the ones used were gotten on a friendly loan. Participants consist of males and females between the ages of 25 to 60 years old. Characteristics of the tested persons is summarized in Tables 3 and 4. Two unique experimental design pathways were followed to check the behavior and evaluate how the proposed method progresses. First, the hand geometry was verified by Hamming distance, and then the bloodstream using MHV.

To establish a scientific experimental backing for the current study, it was vital to check the proposed experimental set-up to those in some existing works. As seen in the experimental design from Reference [28], based on the number of test subjects within this experiment, observations show that data associated with the bloodstream are not very sensitive to temperature when the human body is at rest. While Reference [28] noted that perfusion of blood was better in their study, ours yielded a different result since hand geometry seems to be better in terms of the rate of recognition. In Reference [34], the authors explained in their paper that matching scores are derived from the process of triangulation and binarization using vein structures, and from the distance of knuckle points. The current experimental procedure also follows the route of binarization, but with a convex contour polygon for point location detection. A huge part of this experimental procedure follows the ideas of Reference [33]. The pair worked on finger vein and geometry of the image characteristics. Just like in the case of our experiment, segmentation was done through the location of lines at different finger valleys and linking it to the center of the palm through a convex polygon [33]. This was closely followed by locating feature points through which extraction takes place. By adopting the calculation of the Hamming distance, we distinguish between the feature points of an enrolled and input image respectively.

**Table 3.** Division of tested persons based on gender.


**Table 4.** Division of tested persons based on age.


Forty persons were selected for the testing due to the limited number of available hardware components for image scanning. Each had a hand image tested seven times. The scanners were borrowed only for a limited period of time. As such, only a few testing data was generated. The testing of the system was done on a personal computer with the following parameters (only the parameters that may influence the running of the multi-biometric software are mentioned).

