Learning a Multimodal 3D Face Embedding for Robust RGBD Face Recognition

Machine vision will play a significant role in the next generation of IR 4.0 systems. Recognition and analysis of faces are essential in many vision-based applications. Deep Learning provides the thrust for the advancement in visual recognition. An important tool for visual recognition tasks is Convolution Neural networks (CNN). However, the 2D methods for machine vision suffer from Pose, Illumination, and Expression (PIE) challenges and occlusions. The 3D Race Recognition (3DFR) is very promising for dealing with PIE and a certain degree of occlusions and is suitable for unconstrained environments. However, the 3D data is highly irregular, affecting the performance of deep networks. Most of the 3D Face recognition models are implemented from a research aspect and rarely find a complete 3DFR application. This work attempts to implement a complete end-to-end robust 3DFR pipeline. For this purpose, we implemented a CuteFace3D. This face recognition model is trained on the most challenging dataset, where the state-of-the-art model had below 95% accuracy. An accuracy of 98.89% is achieved on the intellifusion test dataset. Further, for open world and unseen domain adaptation, embeddings learning is achieved using KNN. Then a complete FR pipeline for RGBD face recognition is implemented using a RealSense D435 depth camera. With the KNN classifier and k-fold validation, we achieved 99.997% for the open set RGBD pipeline on registered users. The proposed method with early fusion four-channel input is found to be more robust and has achieved higher accuracy in the benchmark dataset.