Two-Level Attention-based Fusion Learning for RGB-D Face Recognition
This work addresses face recognition for security and biometric applications by improving accuracy through multimodal fusion, though it is incremental as it builds on existing attention and fusion techniques.
The paper tackles RGB-D face recognition by proposing a two-level attention-based fusion method for RGB and depth modalities, achieving classification accuracies over 98.2% on CurtinFaces and 99.3% on IIIT-D RGB-D benchmarks, outperforming state-of-the-art approaches.
With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two-layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results.