Semi-Supervised Cross-Spectral Face Recognition With Small Datasets

Anirudh Nanduri, Rama Chellappa; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024, pp. 588-596

While systems based on deep neural networks have produced remarkable performance on many tasks such as face/object detection and recognition, they also require large amounts of labeled training data. However, there are many applications where collecting a relatively large labeled training data may not be feasible due to time and/or financial constraints. Trying to train deep networks on these small datasets in the standard manner usually leads to serious over-fitting issues and poor generalization. In this work, we explore how a state-of-the-art deep learning pipeline for unconstrained visual face identification and verification can be adapted to domains with scarce data/label availability using semi-supervised learning. The rationale for system adaptation and experiments are set in the following context - given a pretrained network (that was trained on a large training dataset in the source domain), adapt it to generalize onto a target domain using a relatively small labeled (typically hundred to ten thousand times smaller) and an unlabeled training dataset. We present algorithms and results of extensive experiments with varying training dataset sizes and composition, and model architectures using the IARPA JANUS Benchmark Multi-domain Face dataset for training and evaluation with visible and short-wave infrared domains as the source and target domains respectively.