Intelligent multimodal 3D biometric recognition using PointNet + + for robust face-ear authentication

In real-world applications, the reliability of biometric recognition systems that are based on 2D modalities is typically reduced due to limitations such as sensitivity to changes in illumination, facial expressions, and occlusion among other things. To overcome these problems, this research offers a multimodal biometric model that incorporates data from 3D face and 3D ear to achieve reliable identity recognition. The 3D biometrics offer more comprehensive structural information than the 2D characteristics, and they are more resistant to the effects of environmental changes. These 3D features are then used to recognize and secure storage of multimodal biometrics. Initially pre-processing steps, including cropping, normalization, hole filling, and spike removal are applied on 3d biometrics. After that, feature extraction is performed using the PointNet + + model, which is a network based on Convolutional Neural Networks (CNNs) that processes point clouds directly. We used the Face Recognition Grand Challenge (FRGC) database for 3D images of faces and the University of Notre Dame (UND) Collection G database for 3D images of ears for the tests. Our tests show that the PointNet + + model is accurate 99% of the time for 3D face recognition and 98% of the time for 3D ear recognition. With its 3D point cloud optimization and resilient architecture, the PointNet + + model achieves high accuracy for 3D face and 3D ear by learning multi-scale features that capture both local and global information.