Pose-Invariant Learning for Efficient Person Identification from Hyperspectral Hand Images

While person identification from multi/hyperspectral images of hands has advantages such as contactless and high flexibility in capturing images, it remains a difficult task because individual characteristics are not as clear as those of a face or fingerprints. The state-of-the-art method uses a 3D CNN classifier to capture detailed spectral information. However, this is computationally expensive and is negatively affected by undesired spectral variations caused by changes in hand pose. We propose a new method to address these problems. The key technical components of the proposed method are in the introduction of adversarial learning to learn pose-invariant features and in usage of the separable convolutions to decouple the operations in the channel and spatial directions to improve efficiency. Furthermore, these technical components are integrated into a unified supervised contrastive learning framework, which is suitable for person identification. Experimental results demonstrate that our method achieves higher accuracy than the existing method while significantly reducing computational complexity.