Learning similarity and dissimilarity in 3D faces with triplet network

Abstract

Face is the most preferred biometric trait used to recognize a person. The 2D face can be considered as a promising biometric trait; however, it may be affected by changes in the age, skin color, texture, or any other environmental factors like illumination variations, occlusion, and low image resolution. The 3D face is an emerging biometric trait, which is being used recently for human recognition. The discriminating power of the 3D face is highly motivating for many tasks such as security, surveillance, and many other technological application in day to day life. Although there are many techniques available for 3D face recognition, most of these techniques are based on volumetric or depth/range images. The conversion of 3D face data, which is originally in point cloud format to volumetric representation makes the data bulkier. Further, some of the geometric properties may be lost when 3D data is converted to a representation of lower dimensions such as depth/range images. The driving objective behind this research is to perform 3D face recognition by directly using faces represented in the form of 3D point cloud. We propose a novel approach for 3D face recognition by learning the similarity and dissimilarity in 3D faces, and for this purpose, introduce a triplet network. The network is an ensemble of our proposed Convolutional PointNet (CPN) network, used for feature extraction and triplet loss. The proposed network maps a 3D face data to Euclidean space where distance based scores represent the similarity among the 3D faces. We also introduce a new evaluation approach for computing the dissimilarity between highly similar 3D face biometric data. Experimentation has been carried out on two databases, namely IIT Indore 3D face database (our in-house database) and Bosphorus 3D face database. To handle the training issues due to the limited availability of samples for each subject in both the databases, we propose a technique for 3D data augmentation. We perform various experiments using the proposed network and show the performance in terms of verification rate and ROC curve. Our point cloud based triplet network shows encouraging performance as compared to other state-of-the-art techniques. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.