In the past decade, high-security verification based on face analysis has been an important research topic in computer vision and human-machine interaction. There are two types of verification protocols: specific person verification protocols and unspecific person verification protocols. Specific person verification involves verification of specific person, where a population of clients is fixed. Both the adopted representation (features) and the verification algorithms applied in the feature space are based on some training data collected for the client set. Thus, the system design is tuned to the specific client set. In contrast to specific person verification, unspecific person verification is verification of an unspecific person, where the client set is unknown and no samples of the client can be used during the designing stage. In such scenarios, the feature space and the system parameters are trained by subjects that are completely independent from that used for specifying client models.
Past research efforts have mainly focused on the verification of specific clients, not unspecific persons. However in recent years, there is a growing interest in finding a robust and portable (e.g., use the same feature space and the same design parameters (e.g. thresholds)) solution for unspecific person verification. Such a system would be useful in many scenarios. For example, a system for unspecific person verification would be useful in an airport where passengers show their ID cards and the photos on the cards are compared with the on-site video to verify their identities. Another example scenario that would benefit from unspecific person verification is when an existing verification system used on a first group of clients is to be used for a different group of clients.