Fingerprint based identification is one of the most mature and proven technique compare to the all biometric technique. In crime sense fingerprints have been extensively used for identification of criminals. Fingerprints are classified as rolled, plain and latent fingerprints. Latent fingerprints are lifted from surface of objects that are unintentionally touched by person. Latent means poor quality of images or unclear image i. e. smudgy, blurred, moist, small area or fingerprints that contains fewer amount of minutiae. Matching latent fingerprints over rolled or plain fingerprints that is difficult task. Therefore, it is necessary to extract all fingerprint features that are present in latent fingerprint images for accurate matching. Although tremendous progress has been made AFIS (Auto-mated Fingerprint Identification System), this system works well only in that case where rolled and plain fingerprint images. There-fore, semi-automatic system is feasible for feature extraction of la-tent images i. e. some human intervention is allowed during feature extraction from latent fingerprint images. Before feature ex-traction performed it is necessary to improve the quality of la-tent image because they are lifted from any surface and it is easy to extract features from improved quality of latent images. Then outputs candidates are matched over rolled fingerprint images by using fully automatic system. This paper presents study of various techniques that are useful for latent fingerprint matching.

1. S. Prabhakar, "Fingerprint classification and matching using a filter bank", Michigan State University, 2001
2. A. Jain and J. Feng, "Latent fingerprint matching," IEEE Transactions on PAMI, vol. 33, pp. 88, January 2011.
3. A. K. Jain, K. Nandkumar and A. Nagar, "Latent fingerprint matching", Michigan State University, Technical Report MSU-CSE, 07-203, 2007.
4. A. K. Jain, J. Feng, A. Nagar, and K. Nandakumar, "On matching latent fingerprints," Proc. IEEE Computer Vision and Pattern Recognition Workshop Biometrics, pp. 1-8, June 2008
5. J. Feng and A. K. Jain. , "Filtering large fingerprint database for latent matching", In Proceedings of International Conference on Pattern Recognition, pages 1–4, 2008Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
6. S. Yoon, J. Feng, and A. K. Jain, "On latent fingerprint enhancement", In Proceedings of SPIE Biometric Technology for Human Identification, 2010Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
7. Heeseung Choi, MaurilioBoaventura, Ines A. G. Boaventura, Anil K. Jain, "Automatic Segmentation of Latent Fingerprints", IEEE fifth international conference, pages 303-310, 2012. .
8. JianjiangFeng, Soweon Yoon, and Anil K. Jain, "Latent Fingerprint Matching: Fusion of Rolled and Plain Fingerprints", pp 695-704, 2009.
9. Soweon Yoon, JianjiangFeng and Anil K. Jain, "Latent Fingerprint Enhancement via Robust Orientation Field Estimation", IJCB, pp 1-8, 2011.
10. . Soweon Yoon, JianjiangFeng, and Anil K. Jain, "On Latent Fingerprint Enhancement", SPIE Defense, Security, and Sensing: Biometrics Technology for Human Identification Conference, Orlando, FL, April 5-6, 2010

Fingerprint Features Latent Afis Matching Segmentation.