Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers

Most face identification approaches employ a Siamese neural network to compare two images at the image embedding level. Yet, this technique can be subject to occlusion (e.g., faces with masks or sunglasses) and out-of-distribution data. DeepFace-EMD [40] reaches state-of-the-art accuracy on out-of-distribution data by first comparing two images at the image level, and then at the patch level. Yet, its later patch-wise re-ranking stage admits a large O(n
³
log n) time complexity (for n patches in an image) due to the optimal transport optimization.In this paper, we propose a novel, 2-image Vision Transformers (ViTs) that compares two images at the patch level using cross attention. After training on 2M pairs of images on CASIA Webface [58], our model performs at a comparable accuracy as DeepFace-EMD on out-of-distribution data, yet at an inference speed more than twice as fast as DeepFace-EMD [40]. In addition, via a human study, our model shows promising explainability through the visualization of cross-attention. We believe our work can inspire more explorations in using ViTs for face identification.