Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification

201-208Latent fingerprints have become most important evidence in law enforcement department and forensic agencies worldwide. It is also very important evidence in forensic applications to identify criminals as it is mostly encountered in crime scenes. Segmentation is one of the solutions to extract quality features. Fingerprint indexing reduces the search space without compromising accuracy. In this paper, minutiae based rotational and translational features and a global matching approach in combination with local matching is used in order to boost the indexing efficiency. Also, a machine learning (ML) based segmentation model is designed as a binary classification model to classify local blocks into foreground and background. Average indexed time as well as accuracy for full as well as partial fingerprints is tabulated by varying the template sminutiae

Latent Fingerprint Indexing for Faster Retrieval from Dataset with Image Enhancement Technique

730-753Since decades fingerprints have been the prime source in identification of suspects latent fingerprints are compared and examined with rolled and plain fingerprints which are stored in the dataset. The common challenges which are faced while examining latent fingerprints are background noise, nonlinear distortions, poor ridge clarity and partial impression of the finger. As conventional methods of Segmentation doesn’t perform well on latent fingerprints. The current advancement in machine learning based segmentation approach has been showing good results in terms of segmentation accuracy but lacks to provide accurate result in terms of matching accuracy. As one of the problem faced in matching latent fingerprint is low clarity of ridge-valley pattern which results in detection of false minutiae and poor matching accuracy. A multilayer processing of artificial neural network based segmentation is proposed to minimize the detection of false minutiae and increase the matching accuracy. This approach is designed on binary classification model where the simulation will be carried out on IIIT-D latent fingerprint dataset. Segmentation will be divided into full and partial impression fingerprints which are then compared with minutiae with the database using local and global matching algorithm. An improvised result is received which is more accurate as compared to the previous algorithms

Segmentation Techniques through Machine Based Learning for Latent Fingerprint Indexing and Identification

Latent fingerprints have become most important evidence in law enforcement department and forensic agencies worldwide. It is also very important evidence in forensic applications to identify criminals as it is mostly encountered in crime scenes. Segmentation is one of the solutions to extract quality features. Fingerprint indexing reduces the search space without compromising accuracy. In this paper, minutiae based rotational and translational features and a global matching approach in combination with local matching is used in order to boost the indexing efficiency. Also, a machine learning (ML) based segmentation model is designed as a binary classification model to classify local blocks into foreground and background. Average indexed time as well as accuracy for full as well as partial fingerprints is tabulated by varying the template sminutiae

IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA