The face recognition systems are the most widely deployed biometric infrastructure for secured human authentication. However, these systems are vulnerable to a variety of spoof attacks, where assaulters utilize artificially created fake replicas of the human face. To mitigate these attacks, a variety of face anti-spoofing mechanisms are used, where generalization and accuracy of these algorithms are crucial performance protocols. In this research work, we expound an efficient and accurate face anti-spoofing model (i.e. HyFaNet) that integrates feature maps of a pre-trained model with Vision Transformer (ViT). The proposed face anti-spoofing model exploits the potency of the pre-trained model to generate face feature maps and global-level singularities are explored via ViT to yield a robust model. The HyFaNet is trained and evaluated on a benchmark face anti-spoofing dataset and it demonstrates a remarkable performance under unseen scenario with an EER of 0.83%. Moreover, the model exhibits a comparable performance with state-of-the-art (SOTA) face liveness detection methods.