Some biometric traits might be easily captured by an attacker. This is the case of faces, since almost everyone has photos publicly available in social networks like LinkedIN or Facebook. This problem motivates the recent efforts in liveness detection for a secure use of face biometrics. Anti-spoofing methods go from simple ones, for example those based on blink detection, to more complex algorithms for analysing the texture or the light in the scene.
As shown in different publications, these machine learning-based anti-spoofing methods tend to be strongly dependent on the dataset used for training the model. This means that the robustness of the liveness analysis depends on the training dataset (genuine accesses and attacks) and the technology used for face presentation and acquisition, so several concerns appear. Can their behaviour be predicted in the presence of a new attack which has not been taken into account in the training set? Can a single anti-spoofing method be enough to guarantee the security of the system?
Given the cross-dataset analysis in recent publications and real scenario tests it does not seem a good idea entrusting the security of the system to a single anti-spoofing method. This is why we believe the use of a single non-collaborative liveness detection method is not enough for guaranteeing the security of the system in real scenarios, now and in the future, since their robustness is dependent on the presentation technology used by the attacker (video quality measures, light reflectance analysis, etc.).
Alternatively, to counteract presentation attacks, a more robust solution would be the combination of several methods working together and combining automatic analysis tools with user interaction. If the system is able to provoke a reaction in the user and then analyse this reaction, fake attempts using photos or videos from the genuine users could be detected and avoided. Unfortunately, interaction can be a time consuming operation and it could reduce the usability, so the challenge here is to achieve a proper balance between security and convenience. The less perceptible the interaction is, the more usable and difficult to spoof the system will be. Current methods rely on asking the user to perform some action, but we think the future points to unconsciously action-reaction interaction analysis in order to increase both security and usability.