Face recognition systems tend toward ubiquity and are commonly utilized for security purposes. These systems operate based on facial representations, called templates, extracted by a deep neural network from each face image. However, it has been shown that face recognition templates can be inverted to reconstruct underlying face images, posing new security and privacy threats to face recognition systems. To mitigate such attacks against face recognition systems, several biometric template protection schemes have been proposed in the literature. The ISO/IEC 24745 standard requires each biometric template protection scheme to fulfill several requirements, among which non-invertibility is of the utmost importance. Therefore, each of the proposed template protection schemes in the literature used an ad-hoc approach to investigate the invertibility of the protected templates. In this paper, we consider a scenario where an adversary gains knowledge of a template protection scheme as well as its secrets, and tries to reconstruct a face image using a leaked protected template. We consider different template protection schemes, including Bio-Hashing, MLP-Hashing, and Homomorphic Encryption (HE), and reconstruct face images from protected templates. We also use different state-of-the-art face recognition models in both whitebox and blackbox scenarios. To our knowledge, this is the first work on learning-based reconstruction of face images from protected facial templates.