This study explored the use of multi-instance enrollment as a means to improve the performance of 3D face recognition.
Experiments were performed using the ND-2006 3D face data set, which contains 13,450 scans of 888 subjects. This is the largest 3D face data set currently available and contains a substantial amount of varied facial expression. Results indicate that the multi-instance enrollment approach outperforms a state-of-the-art component-based recognition approach, in which the face to be recognized is considered as an independent set of regions. (Publisher Abstract)
Downloads
Similar Publications
- Development and Evaluation of a Nontargeted Electrochemical Surface-Enhanced Raman Spectroscopy (EC-SERS) Screening Method Applied to Authentic Forensic Seized Drug Casework Samples
- Safeguarding Forensic Science Professionals
- Superhydrophobic Surface Modification of Polymer Microneedles Enables Fabrication of Multimodal Surface-Enhanced Raman Spectroscopy and Mass Spectrometry Substrates for Synthetic Drug Detection in Blood Plasma