Over the years, several problems regarding the analysis of face images have been addressed, including face detection, recognition, identification, and verification. The advent of Convolutional Neural Networks (CNNs) gave rise to a drastic improvement on state-of-the-art performances for these problems.

With the increasing popularity of 360º cameras, the demand for models to extract relevant information from spherical images has also emerged. However, traditional CNNs, originally designed for planar images, are typically not suitable for spherical images, as it is necessary to project these spherical images onto a plane, leading to severe distortions. This work presents a method for face verification on spherical images that relies upon CNNs to extract features for training a binary classifier, as well as two new face datasets with spherical images. The effectiveness of our method is assessed through a comparative analysis with relevant planar and spherical CNNs.