Cloud Computing Integrated Multi-Factor Authentication Framework Application in Logistics Information Systems

 As new technology enables firms to perform many daily processes easier the need of authentication and authorization process is becoming an integral part of many businesses. Also mobile applications are very popular nowadays play an important role in our lives. Such demands are not only limited to Logistics Information Systems (LIS) but many field of information system as well. In this study multi-dimensional authentication which consist of online biometric face detection integrated as cloud computing software as a Service (SaaS), Near Field Communication (NFC) card authentication, location confirmation, and temporal data confirmation are gathered together to fulfill different scenarios of authentication needs of business. Microsoft Face API (Application Program Interface, SAAS (software as a service) has been used in face recognition module of developed mobile application. The face recognition module of the mobile application has been tested with Yale Face Database. Location, temporal data and NFC card information are collected and confirmed by the mobile application for authentication and authorization. These images were tested with our facial recognition module and confusion matrices were created. The accuracy of the system after the facial recognition test was found to be 100%. NFC card, location and temporal data authentication not only further increases security level but also fulfils many business authentication scenarios successfully. To the best of our knowledge there is no other authentication model other than implemented one that has a-4-factor confirmation including biometric face identification, NFC card authentication, location confirmation and temporal data confirmation.

Various Approaches of Support vector Machines and combined Classifiers in Face Recognition

In this paper we present the various approaches used in face recognition from 2001-2012.because in last decade face recognition is using in many fields like Security sectors, identity authentication. Today we need correct and speedy performance in face recognition. This time the face recognition technology is in matured stage because research is conducting continuously in this field. Some extensions of Support vector machine (SVM) is reviewed that gives amazing performance in face recognition.Here we also review some papers of combined classifier approaches that is also a dynamic research area in a pattern recognition

Improved Human Face Recognition by Introducing a New Cnn Arrangement and Hierarchical Method

Human face recognition has become one of the most attractive topics in the fields ‎of biometrics due to its wide applications. The face is a part of the body that carries ‎the most information regarding identification in human interactions. Features such ‎as the composition of facial components, skin tone, face\u27s central axis, distances ‎between eyes, and many more, alongside the other biometrics, are used ‎unconsciously by the brain to distinguish a person. Indeed, analyzing the facial ‎features could be the first method humans use to identify a person in their lives. ‎As one of the main biometric measures, human face recognition has been utilized in ‎various commercial applications over the past two decades. From banking to smart ‎advertisement and from border security to mobile applications. These are a few ‎examples that show us how far these methods have come. We can confidently say ‎that the techniques for face recognition have reached an acceptable level of ‎accuracy to be implemented in some real-life applications. However, there are other ‎applications that could benefit from improvement. Given the increasing demand ‎for the topic and the fact that nowadays, we have almost all the infrastructure that ‎we might need for our application, make face recognition an appealing topic. ‎ When we are evaluating the quality of a face recognition method, there are some ‎benchmarks that we should consider: accuracy, speed, and complexity are the main ‎parameters. Of course, we can measure other aspects of the algorithm, such as size, ‎precision, cost, etc. But eventually, every one of those parameters will contribute to ‎improving one or some of these three concepts of the method. Then again, although ‎we can see a significant level of accuracy in existing algorithms, there is still much ‎room for improvement in speed and complexity. In addition, the accuracy of the ‎mentioned methods highly depends on the properties of the face images. In other ‎words, uncontrolled situations and variables like head pose, occlusion, lighting, ‎image noise, etc., can affect the results dramatically. ‎ Human face recognition systems are used in either identification or verification. In ‎verification, the system\u27s main goal is to check if an input belongs to a pre-determined tag or a person\u27s ID. ‎Almost every face recognition system consists of four major steps. These steps are ‎pre-processing, face detection, feature extraction, and classification. Improvement ‎in each of these steps will lead to the overall enhancement of the system. In this ‎work, the main objective is to propose new, improved and enhanced methods in ‎each of those mentioned steps, evaluate the results by comparing them with other ‎existing techniques and investigate the outcome of the proposed system.

A hybrid Face Recognition Method using Markov random Fields

We propose a hybrid face recognition method that combines holistic and feature analysis-based approaches using a Markov random field (MRF) model. The face images are divided into small patches, and the MRF model is used to represent the relationship between the image patches and the patch ID's. The MRF model is first learned from the training image patches, given a test image. The most probable patch ID's are then inferred using the belief propagation (BP) algorithm. Finally, the ID of the test image is determined by a voting scheme from the estimated patch ID's. Experimental results on several face datasets indicate the significant potential of our method. 1

顔画像からの顔姿勢と部位の検出と位置決めの研究

Tohoku University出口光一郎課