Adaptive face recognition for mobility support robots using negative feature augmentation and Growing Neural Gas

Mobility support robots can be made available for use by patients in hospital settings. To prevent the robot from being used by unauthorized patients, an identification system must be installed on the robot. The FaceNet system is unable to recognize users when the camera is positioned to view from below, and the true recognition rate is lower when the threshold is set to a higher level. To address these issues, this paper proposes an adaptive face recognition system with a higher threshold setting and the capacity to adapt to bottom camera viewing angles. To achieve high threshold settings, this paper proposes a novel training strategy that enlarges the output distribution range through Negative Feature Augmentation (NFA) when training a Siamese network. The objective of NFA is to introduce additional negative sample pairs during the training phase, thereby enabling the network to expand the recognition distribution range. This hypothesis is demonstrated on the MNIST dataset. Furthermore, the proposed method was tested on the LFW benchmark dataset, resulting in an improved recognition rate of 0.86 when the threshold was set to 0.8. Subsequently, in order to achieve the ability to adaptively recognize users from bottom perspectives, we introduce the use of Growing Neural Gas (GNG) to track feature changes when the user faces different directions while using the robot. Furthermore, we propose methodologies for aligning eyes and standardizing faces, thereby ensuring that the input face is aligned with the learned features. In our experiments, we collected RGB videos of 16 college students using RT-1, and the results demonstrated a high accuracy rate of 0.80, outperforming other methods.