Face recognition in selfie images is a challenging yet crucial task in computer vision with applications ranging from security systems to social media platforms. In the rapidly evolving field of computer vision, face recognition in uncontrolled settings, such as selfies taken with smartphone cameras, poses unique challenges. This paper examines the efficacy of Vision Transformers (ViT) and its variants, including Class Attention in Transformers (CaiT), CrossViT, MaxViT, MobileViT, Pooling-based Vision Transformer (PiT), and Swin Transformer for face recognition in selfie images. The experiments are performed on the Wild Selfie Dataset (WSD). The dataset features 45,424 selfie images from 42 individuals, characterized by significant variability in expression, lighting, and occlusion, making it a robust testbed for advanced face recognition algorithms. Our study demonstrates that ViTs, leveraging their attention-based mechanisms, surpass the benchmarks set by Convolutional Neural Networks (CNNs) such as VGGFace, VGGFace2, and FaceNet. This paper delineates the performance improvements each ViT variant offers and discusses their potential to streamline face recognition tasks in real-world applications.