The use of face verification systems as a primary source of authentication has been very common over past few years. Despite the advance in face recognition systems, there are still many open breaches left in this domain. One of the practical challenges is to secure face biometric systems from intruder’s attacks, where an unauthorized person tries to gain access by showing the counterfeit evidence (images/videos) in front of face biometric system. We propose a novel approach, which can be easily integrated to the existing face verification systems without any additional hardware deployment. Systems that deliver the power to authenticate persons accurately, swiftly, reliably, without invading privacy issues, cost eﬀectively, in a user-‐friendly manner and without requiring radical modifications to the existing infrastructures are desired. This field of detection of imposter attempts is an open research problem, as more sophisticated and advanced spoofing attempts come into play. Current anti-‐spoofing methods suffer various problems that make them unreliable and inadequate to integrate them with face recognition systems. We shall attack this problem within two research scenarios over the distinct classification schemes: Learning with a large but a single Operational Neural Network (ONN) and ‘Divide and Conquer’ Learning with ensembles of simple but ‘expert’ ONNs, each of which dedicated for a subset of the dataset with a certain homogeneity.