Unconstrained Face Recognition Quality: A Review

Ranbeer Tyagi,Geetam Singh Tomar,Laxmi Shrivastava 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.11

This document signifies the overview of the Unconstrained Face Identification Quality within an unconstrained face-recognition atmosphere. It offers the functions like imbalance, lighting, and phrase variance strategy. It may be employed for both confirmation and id. At the moment, there are lots of means of front watch face-recognition. Couples of years ago, for computer-vision, numerous face-recognition methods were prepared. But, real-globe face-detection quiet deposit a challenging work. The interest in unconstrained useful face-recognition is growing using the detonation of online media for example neighborhood systems, and video-surveillance video wherever encounter evaluation is of great significance. Within this evaluation, we show up to manage reputation within the situation of graph presumption. We are able to identify a mystical encounter utilizing a Diverse Method. This research lights-out the options recommended for unconstrained face recognition quality region and recommending the clear answer to become used by RFG (Reference Face Graph) based face-recognition. Our purpose is RFG centered unconstrained face-recognition to improve the demonstration quality. The Formula and Simulator of recommended sensing methods is likely to be completed by utilizing Mat-laboratory.

Fast Handoff in Wireless Communication Technology

Ranbeer Tyagi,김혜진 사단법인 인문사회과학기술융합학회 2016 예술인문사회융합멀티미디어논문지 Vol.6 No.12

The primary contribution of this work is a detail analysis of the mobile IP fast handoff using sap-reg method. We have seen fast handoff with sap-reg method require very less time delay approximately 3.1 × 10-3 make the connection with best link to new BSs. We also analyze the TCP with FTP traffics and UDP with CBR traffics on various scenarios with different parameter like Throughput, Goodput, and Packet loss rate. Due to this simulation scenario we find that TCP with FTP has better performance (Goodput) for less number of mobile nodes.

Transformation of Image from Color to Gray Scale Using Contrast among DPCM and LMS Method

Ranbeer Tyagi,Geetam Singh Tomar 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8

The solitary greatest mounting investigate fields in the pasture of hypermedia technicality is the transformation of color to grayscale. This paper represents the transformation of color to grayscale with small amount of ASD and PMSE. The unexploited bit may be removing by means of the Differential pulse code modulation (DPCM) in the image for image transformation. We contrast the transformation of image for 1 as well as 3, segments average square distortion and estimation error using DPCM with permanent coefficient and DPCM with LMS algorithm. The outcome are presented which show LMS may present less average square distortion (ASD) and prediction mean square error (PMSE) in transformed image contrast to DPCM with good visual quality. The LMS method was replicated by means of Mat-lab among appreciated to the claim of image exchange contrast by means of DPCM through LMS method. Simple to execute and computationally not expensive is LMS Method. It facet makes the LMS gorgeous designed for image transfer contrast to only DPCM. It is found that a supplementary discriminating grayscale image as compared to established methods converges by the wished-for method.

GABP Neural Network Algorithm Applied in Evaluation of Computer Network Security

Ranbeer Tyagi,Geetam Singh Tomar,Namkyun Baik 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.12

In this paper, in order to assess the risk of network, network security assessment process being involved in the content in detail. The above-mentioned research-based support system platform security test and evaluate research of the safety situation assessment. Prediction subsystem detailed design and carry out the implementation. In this paper, network security issues, as a detailed study of neural networks knowledge. Focus on the evaluation methods and calculation rules of nerve network technology, it has been studied by specific examples. Calculation demonstrated the feasibility of neural network evaluation model through actual case, which pointed out the traditional methods. This paper focuses on the network security assessment based on neural network technology, extensive analysis of the proposed major modeling tool indicator system for network security analysis. The application of neural networks was a network security assessment and to optimize the network by genetic algorithm. The key parameter combination operated efficiency of neural networks to get better play.

Unfamiliar Sides, Video, Image Enhancement in Face Recognition

Ranbeer Tyagi,Geetam Singh Tomar 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.11

The majority of image-processing techniques interact treating the impression as being a two dimensional signal and applying normal signal-processing techniques to it. Within this document we offer a new standard approach to have the hidden restrictions which aren't revealed by the Sobel together Canny filters together in face-recognition environment. We've also showed the effect of the contrast and threshold around the photos. In the same manner, we have shown our techniques about the video. Thus, many beautiful contrast and the hidden data are shared.

Comparative Analysis of Offline Signature Verification System

Deepti Yadav,Ranbeer Tyagi 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.11

A digital signature is a mathematical structure for indicating the validity of digital information or any document. A message is created by a known sender whose digital signature provides a recipient reason, such that the sender cannot reject having sent the message confirmation and that the message was not changed in transportation integrity. The Signature recognition and verification are a behavioral biometric. It can be operated in two various types: one is the Off-Line or Static Signature Verification Technique and another is the On-line or Dynamic Signature Verification Technique. In this paper, we are studying about Off-Line or Static Signature Verification Technique. In this method, users write their own signature on the blank paper and then digitize it with an optical scanner or a camera, and then the biometric system identifies the signature by analyzing its shape and this collection is also called as “off-line” Signature verification. Signature authentication can be divided into three main classes. These classes are based on how alike a forgery is in relation to signature and are identified as random, simple and skilled. In the random forgery the forger does not know about the signer’s shape or signature name. In the simple forgery or unskillful forgery, the forger knows the name of the actual signer but don’t know how his signature looks like. And in the skilled forgery, the forger knows both the information of the signer.

Comparative Analysis of Offline Signature Verification System

Deepti Yadav,Ranbeer Tyagi 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.9

A digital signature is a mathematical structure for indicating the validity of digital information or any document. A message is created by a known sender whose digital signature provides a recipient reason, such that the sender cannot reject having sent the message confirmation and that the message was not changed in transportation integrity. The Signature recognition and verification are a behavioral biometric. It can be operated in two various types: one is the Off-Line or Static Signature Verification Technique and another is the On-line or Dynamic Signature Verification Technique. In this paper, we are studying about Off-Line or Static Signature Verification Technique. In this method, users write their own signature on the blank paper and then digitize it with an optical scanner or a camera, and then the biometric system identifies the signature by analyzing its shape and this collection is also called as “off-line” Signature verification. Signature authentication can be divided into three main classes. These classes are based on how alike a forgery is in relation to signature and are identified as random, simple and skilled. In the random forgery the forger does not know about the signer’s shape or signature name. In the simple forgery or unskillful forgery, the forger knows the name of the actual signer but don’t know how his signature looks like. And in the skilled forgery, the forger knows both the information of the signer.