# Design and Implementation of a Multi-Modal Biometric System for Company Access Control +
## 3 Results
### Face Identification  
To test the face identification phase, the AT&T Face Database was divided into a training set and a test set. The best three face images for each of the 40 users were selected by hand in order to train the LBPH model. The remaining seven images of each user (summing up to a total of 280 images)  
Figure 2: Possible system flows. formed the test set. Those samples were intended to be as the acquired data from the camera sensors in an actual system. Because face recognition can be iterated requiring new (and better) data, the test set has been further divided in two different subsets--namely, \(T1\) and \(T2\). The former contains the four worst images for every user in terms of confidence result: it consists of 160 images that are intended to be dedicated to the first face identification attempt. \(T1\) is used to support the simulation of that identification process which occurs while a user is walking towards the entrance and they are not paying much attention on being recognized. The latter test subset \(T2\) is made up with the remaining 120 images (the remaining three images of each user) of higher quality, and it is used for the controlled face identification (possibly more than once, up to \(k\) times). \(T2\) is then used to support that identification process, which occurs while a user is aware that their first attempt is failed and they are trying to be authenticated again in a more collaborative way (like standing still and keeping their head straight). At first, all the images related to the genuine users (all the images in \(T1\)) were presented to the system, activating the face identification procedure. The frequency of the occurrence for the three possible outcomes is presented in the first row of Table 1. The samples of accepted users obtained a score in the interval \([0,t_{1})]\), and they represent slightly more than the half of the entire data set: from a qualitative point of view, this percentage shows the system as a quite conservative one, allowing people to go on if there is a high evidence about the truthfulness of their claimed identity. The percentage of wrongly rejected genuine users (3.13%) represents the FRR of the face recognition process under unstructured acquisition, and it becomes a lower bound estimation of the FRR of the overall system. Around 43% of the presented data got a score in the interval \([t_{1},t_{3}]\), which is associated to unsolved checks, pushing back for further acquisitions. A similar analysis is reported in the second row of Table 1, using all the images in \(T2\), which are related to further and controlled acquisition of the genuine users. Despite a small growth of the overall FRR by a 1.6%, 81.7% of the data of those subjects that were forwarded to a structured face acquisition phase were correctly classified as genuine, leaving a reasonable fraction to the subsequent iris verification. Table 2 reports--for both \(T1\) and \(T2\)--the percentage of samples which resulted in the MAYBE output value, i.e., the percentage of users that were either sent back to the face analysis (over new acquired data) or moved forward to the iris analysis. With respect \(T1\), only one third of the data--which gave an unsolved check (with a score in \([t_{3},t_{2}]\)--were directly sent to the iris verification phase, while the remaining part (with a score in \([t_{1},t_{3}]\)) was solved using a more controlled face recognition procedure. Moreover, it is worth noting that data in the \(T2\) set never asked for more controlled acquisitions after the first one. A more complete assessment of the goodness of the face identification process requires further analysis of its robustness against not-enrolled subjects that try to overcome the security barriers. Picking 160 face images of male and female subjects of various racial origins from another face database (faces94 [23]), we formed a test set of potential intruders.