Personalized Fingerprint Segmentation

Fingerprint segmentation is an important pre-processing step in automatic fingerprint identification system. Traditional fingerprint segmentation methods either highly depend on empirical thresholds sophistically chosen by experts or a learned model trained by elements generated from manually segmented fingerprints. It is manpower and time consuming. They always try their best to tune their fingerprint segmentation methods to be universal to all unseen fingerprints. However, one fingerprint may have a significantly distinct distribution from another in feature space because fingerprint acquisition is affected by several factors, such as pressure, the types of sensors, finger tip condition (dry, wet etc.). As a result, the delicate threshold and the well trained model may not be suitable to the new input fingerprints from a new finger or a new person. And it makes worse when automatic fingerprint identification systems meet sensor interoperability. To solve the problem, we propose a personalized fingerprint segmentation method: Automatic Labeling based Linear Neighborhood Propagation (ALLNP), which learns a segmentation model special for each input fingerprint image based on the input image only. The proposed method is tested with typical fingerprint images from four heterogeneous data bases of FVC2000. Experimental results show its effectiveness and encouraging strength when fingerprint segmentation meets sensor interoperability.