Robust RGB-D Face Recognition Using Attribute-Aware Loss

TL;DR

This paper introduces an attribute-aware loss function for CNN-based RGB-D face recognition, improving discriminative feature learning by incorporating attribute proximity, leading to enhanced accuracy and robustness especially in real-world conditions.

Contribution

The paper proposes a novel attribute-aware loss function that integrates attribute proximity into CNN training for RGB-D face recognition, addressing bias and attribute correlation issues.

Findings

Depth channel and attribute-aware loss improve recognition accuracy.

Model trained on large-scale RGB-D dataset with 100K+ identities.

Enhanced robustness in real-world face recognition scenarios.

Abstract

Existing convolutional neural network (CNN) based face recognition algorithms typically learn a discriminative feature mapping, using a loss function that enforces separation of features from different classes and/or aggregation of features within the same class. However, they may suffer from bias in the training data such as uneven sampling density, because they optimize the adjacency relationship of the learned features without considering the proximity of the underlying faces. Moreover, since they only use facial images for training, the learned feature mapping may not correctly indicate the relationship of other attributes such as gender and ethnicity, which can be important for some face recognition applications. In this paper, we propose a new CNN-based face recognition approach that incorporates such attributes into the training process. Using an attribute-aware loss function that regularizes the feature mapping using attribute proximity, our approach learns more discriminative features that are correlated with the attributes. We train our face recognition model on a large-scale RGB-D data set with over 100K identities captured under real application conditions. By comparing our approach with other methods on a variety of experiments, we demonstrate that depth channel and attribute-aware loss greatly improve the accuracy and robustness of face recognition.