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Free Download Face Fudicial Detection Project in Hibernate framework with source code

Hibernate framework project   Publish on -  2020-01-21
real-life
siddharth lahan
ABAP java 
0 Reviews 0
1930 View
91 Downloads

Face Fudicial Detection project is a mobile application which is developed in Hibernate framework platform. This Hibernate framework project with tutorial and guide for developing a code. Face Fudicial Detection is a open source you can Download zip and edit as per you need. If you want more latest Hibernate framework projects here. This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project. Zip file containing the source code that can be extracted and then imported into jdk. This Source code for BE, BTech, MCA, BCA, Engineering, Bs.CS, IT, Software Engineering final year students can submit in college. This script developed by siddharth lahan. This mobile application 100% working smooth without any bug. It is developed using java,c++ and Database firebase. This software code helpful in academic projects for final year students. We have a great collection of Hibernate framework projects.

Document and Reports information

This doucment file contains project Synopsis, Reports, and various diagrams. Also abstract pdf file inside zip so that document link below the page. Class Diagrams, Use Case Diagrams, Entity–relationship(ER) Diagrams, Data flow diagram(DFD), Sequence diagram and software requirements specification (SRS) in report file. Complete ready made open source code free of cost download. You can find Top Downloaded Hibernate framework projects here.

About Project

project Name Face Fudicial Detection
project ID 3528
Developer Name siddharth lahan
Publish Date 2020-01-21
project Platform Hibernate framework
Programming Language java,c++
Front End 
Back End 
IDE Tool jdk
project Earning siddharth lahan Earn Rs.0 from this project.
Database firebase
project Type mobile Application
No of project Download 91
project Total View 1930
Today Trends 1
Current Month Trends 42
Last Month Trends 27

You have any error or you don't understand project follow or any other problem.You can ask question. you know any answer or solution then give a answer and help other student.Complete they project perfectly.

Features of the Project

Its a project on detection of face using real time . great project.Facial fiducial detection is a challenging problem for several reasons like varying pose, appearance, expression, partial occlusion and others. In the past, several ap- proaches like mixture of trees [32], regression based meth- ods [8], exemplar based methods [7] have been proposed to tackle this challenge. In this paper, we propose an exemplar based approach to select the best solution from among outputs of regression and mixture of trees based algorithms (which we call can- didate algorithms). We show that by using a very simple SIFT and HOG based descriptor, it is possible to identify the most accurate fiducial outputs from a set of results pro- ducedbycandidatealgorithmsonanygiventestimage. Our approach manifests as two algorithms, one based on opti- mizing an objective function with quadratic terms and the other based on simple kNN. Both algorithms take as input fiducial locations produced by running state-of-the-art can- didate algorithms on an input image, and output accurate fiducials using a set of automatically selected exemplar im- ages with annotations. Our surprising result is that in this case, a simple algorithm like kNN is able to take advantage of the seemingly huge complementarity of these candidate algorithms, better than optimization based algorithms. We do extensive experiments on several datasets, and show that our approach outperforms state-of-the-art con- sistently. In some cases, we report as much as a 10% im- provement in accuracy. We also extensively analyze each component of our approach, to illustrate its efficacy. An implementation and extended tech- nical report of our approach is available www.sites.google.com/site/wacv2016facefiducialexemplars

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