Discriminant Patch Representation for RGB-D Face Recognition Using Convolutional Neural Networks
This addresses face recognition for security or biometric applications, but it is incremental as it applies existing CNN methods to RGB-D data.
The paper tackled face recognition using RGB-D data by training Convolutional Neural Networks to learn discriminant patch features, achieving competitive results compared to standard hand-crafted feature extractors on state-of-the-art datasets.
This paper focuses on designing data-driven models to learn a discriminant representation space for face recognition using RGB-D data. Unlike hand-crafted representations, learned models can extract and organize the discriminant information from the data, and can automatically adapt to build new compute vision applications faster. We proposed an effective way to train Convolutional Neural Networks to learn face patch discriminant features. The proposed solution was tested and validated on state-of-the-art RGB-D datasets and showed competitive and promising results relatively to standard hand-crafted feature extractors.