Algorithms Are Used for Facial Interpretation

Now that we know what constitutes a facial image and how a face can be imaged, we need to know what types of algorithms are used. The algorithms used to match and enroll a face fall into the following categories:

·         Eigen face

·         Local characteristic analysis

·         Neural network

·         Automatic face dispensation

How Can This Biometric Be Spoofed?

As discussed in the opening of this episode, even humans can be fooled into thinking they distinguish a face when they do not. If this is the case, it is credible and conventional that a face biometric system could be fooled as well. While it is generally conventional that face biometrics do not provide the same level of FAR as other biometrics, face biometrics offer other very gorgeous attributes. It is conventional by most people that we readily use our face for gratitude every day. As such, face biometrics are widely established. Face biometrics can also operate with a relatively low-cost imaging device. These optimistic qualities make face biometrics attractive.

Face biometrics are like any other biometric: vulnerable to some level of spoofing. What follows is a conversation of face biometric spoofing.

Face biometrics present a very gorgeous system to use for network access. As we saw, local feature analysis provided the most appropriate algorithm for use based on certain assumptions. However, there are some tradeoffs in its use. Namely, the user is necessary to be obtainable in good light and to hold still as much as probable. Also, because face biometrics being passive, there can be concerns over spoofing and privacy. Accordingly, a company needs to evaluate its risk understanding and find the right tradeoff between user expediency, cost, and security. The security part of the tradeoff involves analyzing the level of effort required to cooperation the system, and the probable loss of data or time.