Congratulations to Jian Li for passing his defense on November 29, 2016. His Ph.D. thesis is entitled “Advanced Techniques for Latent Fingerprint Enhancement and Recognition”. Here is an abstract of his thesis and we are so glad to have him share his Ph.D. experience as well.
Fingerprints provide one of the most popular biometric data, and have been widely used in individual person identification and verification. The Automated Fingerprint Identification System (AFIS) offers important evidences for criminal investigation, and serves as an important tool for law enforcement. As compared with conventional exemplar fingerprints, latent fingerprints are typically collected in a crime scene. They are often degraded and corrupted with very poor quality, leading to very low identification rates. In a practical system, a latent fingerprint has to be enhanced prior to feature extraction to ensure a reliable fingerprint matching performance. In this research, we study techniques for latent fingerprint enhancement and orientation field estimation to achieve a higher matching rate. Our studies include traditional image processing techniques as well as a new method based on the emerging convolutional neural network (CNN).
First, we propose a new method using the Markov random field (MRF) model and the sparse representation (SR) of ridges to enhance latent fingerprint. The proposed MRF-SR method is inspired by the recent success of dictionary-based methodologies in recent fingerprint community. Second, we extend the MRF-SR method in order to extract the orientation field of fingerprint. As an essential feature of fingerprints, the orientation field can enhance a fingerprint image with directional and contextual filtering. It provides a valuable supplementary tool to other orientation estimation algorithms in the literature. Finally, we explore the feasibility and study the performance of applying the CNN to latent fingerprint [...]