Abstract
Heterogeneous face recognition (HFR) aims to match face images across different imaging domains such as visible-to-infrared and visible-to-thermal. Recently, the increasing utility of nonvisible imaging has increased the application prospects of HFR in areas such as biometrics, security, and surveillance. HFR is a challenging variate of face recognition due to the differences between different imaging domains. While the current research has proposed image preprocessing, feature extraction, or common subspace projection for HFR, the optimization of these multi-stage methods is a challenging task as each step needs to be optimized separately and the performance error accumulates over each stage. In this paper, we propose a unified end-to-end Cross-Modality Discriminator Network (CMDN) for HFR. The proposed network uses a Deep Relational Discriminator module to learn deep feature relations for cross-domain face matching. Simultaneously, the CMDN is used to extract modality-independent embedding vectors for face images. The CMDN parameters are optimized using a novel Unit-Class Loss that shows higher stability and accuracy over other popular metric-learning loss functions. The experimental results on five popular HFR datasets demonstrate that the proposed method achieves significant improvement over the existing state-of-the-art methods.
Highlights
While the performance of facial recognition (FR) algorithms has achieved near-human accuracy for frontal images, the performance is limited in scenarios involving extreme variations in illumination, expression, pose, presentation attacks, and disguises [2–4]. e use of infrared, thermal, and 3D imaging is being explored to overcome the limitations of visible modality. ese modalities have shown advantages against pose, illumination, spoofing, and disguise
E increasing application of different modalities for image acquisition results in an abundance of face data where the images belong to different imaging domains. is leads to scenarios where images from different domains need to be matched for face recognition. e infrared spectrum is robust to illumination, making it ideal for capturing images in the dark. is makes infrared imaging suitable for security cameras, surveillance, and monitoring around the clock
While deep perceptual mapping (DPM) and CpGaN aim to find a translation from thermal to visible images, the proposed method focuses on identity feature extraction and learning the relationships between those features
Summary
In addition to robustness to illumination, thermal imagery shows promise against disguise [5] and spoofing [6], making it suitable for security-sensitive applications of FR. Ese advantages of visible and thermal domains have led to an increased usage of these modalities for applications of FR. Is leads to scenarios where images from different domains need to be matched for face recognition. E scenario in which the modality of the probe images is different from that of the enrollment image is known as heterogeneous face recognition (HFR) [8], e.g., visible-to-infrared (VIS-NIR), visible-to-thermal (VISTHE), and visible-to-sketch. The large modality gap between the two domains makes visiblethermal face recognition a challenging task. Instead of using a distance-based loss function for face matching, the network employs a Deep Relational Discriminator (DRD) module to learn the relationships between cross-domain images. We formulate a metric learning-based loss, namely, Unit-Class Loss that is robust to a small amount of training data, noisy samples, and large modality differences in the training data. e proposed loss enhances the feature learning of the network by considering individual samples as well as the whole class distributions
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