Contactless 3D fingerprint identification has gained significant attentions in recent years as it can offer more hygienic, accurate and ubiquitous personal identification. Despite such advantages, contactless 3D imaging often results in partial 3D fingerprints as it requires relatively higher cooperation from users during the contactless 3D imaging. Such contactless 3D fingerprint images significantly degrade matching accuracy due to partial 3D fingerprint imaging. This paper proposes an end-to-end contactless 3D fingerprint representation learning model based on convolutional neural network (CNN). The proposed model includes one fully convolutional network for fingerprint segmentation and three Siamese networks to learn multi-view 3D fingerprint feature representation. Contactless partial 3D fingerprint identification is a more challenging problem due to its high degree of freedom during contactless 3D fingerprint acquisition and is also addressed by using proposed model. We therefore investigate multi-view 3D fingerprint recognition and partial 3D fingerprint using proposed approach. Comparative experimental results, presented in this paper using state-of-the-art 3D fingerprint recognition method, demonstrate the effectiveness of the proposed multi-view approach and illustrate a significant improvement of state-of-the-art 3D fingerprint recognition methods.
- Contactless 3D fingerprint recognition
- Multi-view CNN
- Partial 3D fingerprint identification
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence