In 1981,Lamport proposed the first well-known remote password authentication scheme using smart cards.A number of remote password authentication schemes with smart cards have been present since then.Recently Shen,Lin and Hwang pointed out a different type of attack on this scheme and presented a modified scheme to remove these defects.In this paper we present a new remote user authentication scheme which is the modified form of the Shen-Lin-Hwang's scheme.In this scheme the password is controlled by the user,and at any time can be changer.The scheme can withstand attack that similar to Chan and Cheng's attack and Chang and Hwang' attack in registration phase and authentication phase.

A Modified Remote User Authentication Scheme Using Smart Cards

A novel interactive technique for extraction of text characters from the images of stone inscriptions is introduced in this paper. It is designed particularly for on-site processing of inscription images acquired at various historic palaces, monuments, and temples. Its underlying principle is made of several robust character-analytic elements like HoG features, vowel diacritics, and location-bounded scan lines. Since the process involves character spotting and extraction of the inscribed information to editable text, it would subsequently help the archaeologists for epigraphy, transliteration, and translation of rock inscriptions, particularly for the ones having high degradations, noise, and a variety of styles according to the mason origin and reign. The spotted characters can also be used to create a database for ancient script analysis and related archaeological work. We have tested our method on various stone inscriptions collected from some of the heritage sites of Karnataka, India, and the results are quite promising. An Android application of the proposed work is also developed to aid the epigraphers in the study of inscriptions using a tablet or a mobile phone.

A Method for Extracting Text from Stone Inscriptions Using Character Spotting

In this paper we have presented a technique for enhancement and retrieval of historic inscription images. Inscription images in general have no distinction between the text layer and background layer due to absence of color difference and possess highly correlated signals and noise; pertaining to which retrieval of such images using search based on feature matching returns inaccurate results. Hence, there is a need to first enhance the readability and then binarize the images to create a digital database for retrieval. Our technique provides a suitable method for the same, by separating the text layer from the non-text layer using the proposed cumulants based Blind Source Extraction(BSE) method, and store them in a digital library with their corresponding historic information. These images are retrieved from database using image search based on Bag-of-Words(BoW) method.

Enhancement and Retrieval of Historic Inscription Images

The increasing need for better authentication methods against hackers has called for the use of the biometric authentication method to guard against unauthorized access into the systems. The used of human characteristics for biometrics provides authentication for different kind of systems. However, poor quality of authentication still allows hackers gaining access to these systems. Many biometrics authentication approaches have been proposed to improve the authentication accuracy and other related quality measures. This survey aims to provide a state-of-the-art fingerprint and password biometric authentication approaches. Their challenges have been presented and discussed in terms of biometric authentication. Furthermore, the strengths and weaknesses of each of the fingerprint and password biometric authentication are discussed and compared. The findings show that fingerprint image quality and password authentication is still an active research area where performance requires improvement. Also, the graphical password indicates a promising future direction for enhancing password methods.

https://www.accentsjournals.org/PaperDirectory/Journal/IJACR/2020/3/4.pdf

A survey of biometric approaches of authentication

In recent years, the automotive field has been changed by the accelerated rise of new technologies. Specifically, autonomous driving has revolutionized the car manufacturer's approach to design the advanced systems compliant to vehicle environments. As a result, there is a growing demand for the development of intelligent technology in order to make modern vehicles safer and smarter. The impact of such technologies has led to the development of the so-called Advanced Driver Assistance Systems (ADAS), suitable to maintain control of the vehicle in order to avoid potentially dangerous situations while driving. Several studies confirmed that an inadequate driver's physiological condition could compromise the ability to drive safely. For this reason, assessing the car driver's physiological status has become one of the primary targets of the automotive research and development. Although a large number of efforts has been made by researchers to design safety-assessment applications based on the detection of physiological signals, embedding them into a car environment represents a challenging task. These mentioned implications triggered the development of this study in which we proposed an innovative pipeline, that through a combined less invasive Neuro-Visual approach, is able to reconstruct the car driver's physiological status. Specifically, the proposed contribution refers to the sampling and processing of the driver PhotoPlethysmoGraphic (PPG) signal. A parallel enhanced low frame-rate motion magnification algorithm is used to reconstruct such features of the driver's PhotoPlethysmoGraphic (PPG) data when that signal is no longer available from the native embedded sensor platform. A parallel monitoring of the driver's blood pressure levels from the PPG signal as well as the driver's eyes dynamics completes the reconstruction of the driver's physiological status. The proposed pipeline has been tested in one of the major investigated automotive scenarios i.e., the detection and monitoring of pedestrians while driving (pedestrian tracking). The collected performance results confirmed the effectiveness of the proposed approach.

/pdf/deep-neuro-vision-embedded-architecture-for-safety-3p8r7mwmdn.pdf

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

Interactive retail is one of the biggest technology trends in recent years. Machine Learning based object detection and Aug mented Reality have proven as major technological advancements in the retail industry. This proposed research work aims at overcoming several setbacks in the present scenario such as high labor costs, time consuming process of purchasing the products, helping the customers to maintain their specified budgets and language barriers while selling products to groups with different linguistic backgrounds and by combining the real-world interaction of Augmented Reality and Machine Learning-based product identification. The proposed work provides customers with a futuristic shopping experience while maintaining their allowed budgets. The Machine Learning-based product identification approach detected objects with around 96% accuracy and Vuforia-based Augmented Reality approach detected objects with maximum accuracy.

Augmented Reality and Machine Learning based Product Identification in Retail using Vuforia and MobileNets

This paper addresses the problems encountered during digitization and preservation of inscriptions such as perspective distortion and minimal distinction between foreground and background. In general inscriptions possess neither standard size and shape nor colour difference between the foreground and background. Hence the existing methods like variance based extraction and Fast ICA based analysis fail to extract text from these inscription images. Natural gradient flexible ICA (NGFICA) is a suitable method for separating signals from a mixture of highly correlated signals, as it minimizes the dependency among the signals by considering the
slope of the signal at each point. We propose an NGFICA based enhancement of inscription images. The proposed method improves word and character recognition accuracies of the OCR system by 65.3% (from 10.1% to 75.4%) and 54.3% (from 32.4% to 86.7%), respectively.

/pdf/ngfica-based-digitization-of-historic-inscription-images-42s52ls99v.pdf

NGFICA Based Digitization of Historic Inscription Images

This paper addresses the problems encountered during digitization and preservation of inscriptions such as perspective distortion and minimal distinction between foreground and background. In general inscriptions neither possess standard size and shape nor colour difference between the foreground and background. Hence the existing methods like variance based extraction and Fast-ICA based analysis fail to extract text from these inscription images. Natural gradient Flexible ICA (NGFICA) is a suitable method for separating signals from a mixture of highly correlated signals, as it minimizes the dependency among the signals by considering the slope of the signal at each point. We propose an NGFICA based enhancement of inscription images. The proposed method improves word and character recognition accuracies of the OCR system by 65.3% (from 10.1% to 75.4%) and 54.3% (from 32.4% to 86.7%) respectively.

Enhancement of inscription images

Multi-object tracking is a significant domain in Computer vision applications, which involves giving unique identities to different objects, and maintaining the association between them, for real-time applications. However, most of the trackers fail to achieve decent levels of accuracy as well as speed. In this paper, we propose a tracking technique, which utilizes both high-speed detections from Yolo, as well as deep feature extraction, from a convolutional neural network. The extracted features, along with position vectors and color histograms, are matched between corresponding frames, to develop an association between pedestrians. It can face issues like slight changes in object appearance in continuous frames, such as shape, size or illumination changes, partial occlusions, or re-identification of pedestrians, on re-entering the view, or after being occluded, for a certain length of frames. We have used the Yolo framework for fast object detection, a MobileNet architecture based custom CNN for feature extraction, and a set of algorithms to generate associations between frames. On the publically available Towncentre dataset, our framework can reach a MOTA of 93.2%.

Real-time Pedestrian Tracking based on Deep Features

The recent cybercrimes growth is a serious issue, millions of people become the victim of cybercrime and most of them cannot be prevented easily just by strong passwords. The attacks include key logging, brute-forcing, guessing attacks, replay attack. In this paper, we will discuss an authentication scheme in which using an android phone we can tackle any such attacks and provide a secure authentication over insecure and low-security networks.

Geetanjali Bhola

Papers

Augmented Reality and Machine Learning based Product Identification in Retail using Vuforia and MobileNets

NGFICA Based Digitization of Historic Inscription Images

Enhancement of inscription images

Real-time Pedestrian Tracking based on Deep Features

Dynamic password authentication protocol using android device and one-way function