A latent fingerprint is an interesting issue because of it has attained from crime places and moreover contained a low quality image, less number of features and unwanted noises. It is necessity to extract the original image with exact boundary from the surface for further processing such as authentication, identification and matching. In this work, a new distance measure has been proposed for latent fingerprint edge detection using Intuitionistic Type-2 Fuzzy Entropy IT2FE and a comprehensible definition is made for Intuitionistic Type-2 Fuzzy Sets IT2FS. IT2FS takes into account of uncertainty in the form of membership function which is also termed as Intuitionistic Type-2 Fuzzy Divergence IT2FD. The experiment is conducted with public domain fingerprint databases such as FVC-2004 and IIIT-latent fingerprint. The edge detection is carried out with the proposed method and the results are discovered better regarding existing method.

Fingerprint classification is as yet difficult issue due to incomplete information in ridges and low quality images. To address these problems, we propose a new and robust Local Adjacent Extrema Pattern (LAEP) is a feature descriptor for fingerprint classification. The proposed descriptor finds it values based on indexes of local adjacent extremas using first order derivatives. The intensity values of the local extremas are compared with center pixel intensity value to employ the correlation of central pixel with its neighbors. Finally, the descriptor is generated on the support of the indexes and local extremas values. Support Vector Machine (SVM) is utilized for classification of fingerprint images into five classes. To prove the effectiveness of the proposed descriptor, we have tested on Indraprastha Institute of Information Technology (IIIT) - Indian rural fingerprint database for classification. In addition, the classification results of the proposed descriptor are compared with the existing methods. The resultant LAEP descriptor proved better classification accuracy than the previous methods.