The quality of a face crop in an image is decided by many factors such as
camera resolution, distance, and illumination condition. This makes the
discrimination of face images with different qualities a challenging problem in
realistic applications. However, most existing approaches are designed
specifically for high-quality (HQ) or low-quality (LQ) images, and the
performances would degrade for the mixed-quality images. Besides, many methods
ask for pre-trained feature extractors or other auxiliary structures to support
the training and the evaluation. In this paper, we point out that the key to
better understand both the HQ and the LQ images simultaneously is to apply
different learning methods according to their qualities. We propose a novel
quality-guided joint training approach for mixed-quality face recognition,
which could simultaneously learn the images of different qualities with a
single encoder. Based on quality partition, classification-based method is
employed for HQ data learning. Meanwhile, for the LQ images which lack identity
information, we learn them with self-supervised image-image contrastive
learning. To effectively catch up the model update and improve the
discriminability of contrastive learning in our joint training scenario, we
further propose a proxy-updated real-time queue to compose the contrastive
pairs with features from the genuine encoder. Experiments on the low-quality
datasets SCface and Tinyface, the mixed-quality dataset IJB-B, and five
high-quality datasets demonstrate the effectiveness of our proposed approach in
recognizing face images of different qualities.
Improving Mixed-Quality Face Recognition with Quality-Guided Joint Training
In the field of multimedia information systems, face recognition has always been a challenging problem, particularly when dealing with mixed-quality face images. The quality of a face crop in an image is influenced by various factors, including camera resolution, distance, and illumination condition. Discriminating face images with different qualities poses a difficult task in realistic applications.
Traditional approaches to face recognition have been designed specifically for either high-quality (HQ) or low-quality (LQ) images. However, when applied to mixed-quality images, these approaches tend to perform poorly. Moreover, many existing methods require pre-trained feature extractors or auxiliary structures to support training and evaluation.
In this paper, the authors propose a novel quality-guided joint training approach for mixed-quality face recognition. The key idea is to apply different learning methods based on the qualities of the images. This approach enables simultaneous learning of HQ and LQ images using a single encoder.
For HQ data learning, a classification-based method is employed based on quality partitioning. This allows for better understanding and interpretation of HQ images. On the other hand, LQ images lack identity information, so the authors propose learning them using self-supervised image-image contrastive learning.
To address the challenge of model update and improve the discriminability of contrastive learning in the joint training scenario, the authors propose a proxy-updated real-time queue. This queue is used to compose contrastive pairs with features from the genuine encoder. This ensures that the model keeps up with updates and enhances the effectiveness of contrastive learning.
The proposed approach is evaluated using various datasets, including low-quality datasets such as SCface and Tinyface, a mixed-quality dataset called IJB-B, and five high-quality datasets. The experiments demonstrate the effectiveness of the proposed approach in recognizing face images of different qualities.
Multi-disciplinary Nature and Related Concepts
This research on mixed-quality face recognition combines concepts and techniques from various disciplines. It leverages principles from computer vision, machine learning, and multimedia information systems to address the challenge of discriminating face images with different qualities.
Furthermore, this study is closely related to the broader field of multimedia information systems, as it deals with the analysis and understanding of visual content, specifically face images. It incorporates techniques for image quality assessment, feature extraction, and learning methods to improve the recognition of face images of different qualities.
In addition, the proposed approach has implications for animations, artificial reality, augmented reality, and virtual realities. Face recognition is a fundamental component in these domains, and advancements in mixed-quality face recognition can enhance the realism and accuracy of facial animations and virtual environments. By applying different learning methods according to image qualities, the proposed approach contributes to improving the overall quality and fidelity of multimedia systems involving virtual representations of human faces.
Overall, this research presents a novel quality-guided joint training approach for mixed-quality face recognition. It demonstrates the importance of considering different learning methods based on image qualities to achieve better performance. With its multidisciplinary nature and relevance to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, this study opens up new possibilities for advancing face recognition technologies and enhancing various applications in visual computing.