## Multifactor Authentication

Authentication can be accomplished with one factor, two factors, or multiple factors. Which one is the weakest level of authentication and which is the most secure and why? When would a more secure system be required? Be able to explain these multifactor authentication methods: password protection, token presence, voice biometrics, facial recognition, ocular-based methodology, hand geometry, vein recognition, fingerprint scanner, thermal image recognition, and geographical location. What are some challenges of multiple factor authentication when using biometrics? There is a lot of interesting information covered in this article that you do not need to memorize, but that you should be aware of.

### 4.3. Proposed MFA Solution for V2X Applications

#### 4.4.2. Probabilistic Decision Methodology

The sensor responds with a result of its measurements as well as a probabilistic characteristics. Further, the data is merged before the final decision is made. Therefore, the entire set of the measured data could be utilized when making a group decision and, accordingly, a common result might be established based on the set collected from all sensors.

In the second case, the sensor returns a result of the measurements as well as the template comparison in the form of a match score $z_{i}$ $(0 \leq z_{i} \leq 1)$. For each of the values $z_{i}$, the conditional probability $P\left(z_{i} \mid H_{0}\right)$ is calculated based on the $F A R_{i}$ values at $z_{i}$. In addition, the conditional probability $P\left(z_{i} \mid H_{1}\right)$ is determined by $F R R_{i}$ values at $z_{i}$.

This approach offers an opportunity to consider the strict decision methodology as a simplified model of the probabilistic one for the case where $F A R_{i}$ and $F R R_{i}$ are given only in one point. Here, the measurement result can only take two values, i.e., higher or lower than the selected threshold.