Latent Fingerprint Segmentation Using Modified ADTVM Model

Latent finger print identification has many roles in identifying and convicting criminals. Rolled and plain prints are obtained in an attended mode so that they are usually of good visual quality and contain sufficient information for reliable matching. On the other hand, latent prints are usually collected from crime scenes and often mixed with other components such as structured noise or other fingerprints. Existing fingerprint recognition algorithms fail to work properly on latent fingerprint images. Here we purpose a ADTV model for latent finger print segmentation. The proposed ADTV model decomposes a latent fingerprint image into two layers: cartoon and texture. The cartoon layer contains unwanted components (e. g. , structured noise) while the texture layer mainly consists of the latent fingerprint. This cartoon-texture decomposition facilitates the process of segmentation, as the region of interest can be easily detected from the texture layer using traditional segmentation methods