# Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
## 4 Biometric Identification as a Unique Opportunity
### Digital Identity Verification with eID Card's Biometric Data  
The image of the face stored in the eID card's chip (referred to here as gallery image) is freely accessible and can be easily retrieved with a specific API. Different mobile applications that can read the NFC chip and retrieve this data can be freely downloaded from the app stores. A very effective method for verifying if a person is the real owner of the digital identity is to compare the gallery image with a photo taken during the authentication with the camera of the mobile device (referred to here as probing image). It could be argued that with modern technologies, like hyper-trained AI algorithms, a 1:1 verification is a relatively easy task to accomplish. Unfortunately, it is not always the case. As reported by the NIST in its Facial Recognition Vendor Test (FRVT) [30], facial recognition systems can achieve near-perfect accuracy under ideal conditions, reaching accuracy scores as high as 99.9999%. The ideal conditions with regard to face images (such as passport photos or mugshots) are defined by the ISO Standard for Biometric Data Interchange [31], which require consistency in lighting and head pose as well as clear and unobscured facial features of the subject. Also, eID cards are compliant with this standard and contain a very clear picture of the face, taken by an operator in a controlled environment (without noise in the background and with full direct light) with specific recommendations (do not cover the face, do not make facial expressions, etc.). The quality of the picture is at the end of the process ensured by the operator. Moreover, the data are stored within the secure element of the eID card in accordance with standard security measures, such as those defined by ICAO 9303 and Regulation (EU) 2019/1157 of the European Parliament and of the Council. However, if images taken in the real world with commercial devices are used, facial recognition systems have a degree of accuracy far lower. The quality of the probing image acquired with the \begin{table}
\begin{tabular}{c c} \hline \hline
**Access Mode** & **Data** \\ \hline Freely & - unique card identifier (NIS) - name and surname - date and place of birth - gender and nationality \\ \hline Scanning the MRZ code or by typing the CAN number (both printed on the card) & - unique card identifier (NIS) - name and surname - date and place of birth - gender and nationality \\ \hline Typing the PIN (provided separately) & - TLS client certificate \\ \hline Only by law enforcement & - fingerprints \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The data included in the national electronic ID card (eID) and the related access mode can be illustrated using the example of the Italian CIE.  
mobile device is extremely dependent on the precision of the camera. Furthermore, the acquisition takes place in uncontrolled environments, with noisy backgrounds, variable light conditions or shadows, and uncontrolled subjects who may not be looking directly at the camera. These conditions are often called \"in the wild\". According to the FRVT [30], the incorrect rejection rate of the leading matching algorithms goes from 0.0001% when using VISA photos to 0.27% when using pictures of faces captured \"in the wild\", resulting in systems with less usability. In the following section we propose a novel solution to improve the performance of a facial recognition system, exploiting the eID's biometric data. More details about the implemented solution can be found at [32]. The solution envisages 3D reconstruction technologies, such as monocular reconstruction and depth sensor scans, to create a 3D model of the user's head; such a model, cleaned of environmental noises (light conditions, background, others), can be used to generate a frontal snapshot of the face, to be compared with the CIE's image.