Segmentation of Latent Fingerprint using Neural Network
Neha Chaudhary1, Priti Dimri2, Harivans Pratap Singh3

1Neha Chaudhary*, Ph.D Scholar UTU Dehradun, India.
2Priti Dimri, Department of Computer Science and Applications, G.B. Pant Engineering College, Ghurdauri, Uttarakhand, India.
3Harivans Pratap Singh,  Asst. Prof. Department of Computer Science & Engineering ABES Engineering College ,Ghaziabad, India.
Manuscript received on September 18, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3777-3780 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9820109119/2019©BEIESP | DOI: 10.35940/ijeat.A9820.109119
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Abstract: Latent fingerprints are the fingerprints that are left by the criminal unintentionally on the surface of the crime scene. The qualities of the latent fingerprints are very poor due to the overlapping patterns and structured noises. Latent fingerprint segmentation is a difficult task due to low visibility, structured noise, and complex structure. In this paper, a fusion of morphological and neural network approach is purposed for latent fingerprint segmentation. This method automatically segments the fingerprints and non-fingerprints patterns without human intervention. The morphological method is used for segmentation of the fingerprint region. Fingerprint region then divides into y*y blocks and extracts the features of each block and uses them as an input of NN to classify the blocks into fingerprint and non-fingerprint blocks. We are using the IIIT-D database and the shows that this model batters then the existing model.
Keywords: Fingerprint Segmentation, Fingerprint Image Pre-Processing, Image Recognition, Ridge Orientation, Total variation.