Jyoti Malik

Harris Operator Corner Detection using Sliding Window Method

In this paper, Harris Corner Detector is proposed as a corner detection technique to extract palmprint features in the form of corners. Here, hamming distance similarity measurement using sliding window method is used as a feature matching method for the corners detected. The aim of using hamming distance method for corner matching is the non-dependency of the method with the number of corners detected. So, the comparison (matching) time will be constant with hamming distance feature matching method. We used the same feature matching technique in edge detection and got good results. In this paper, palmprint features are analyzed on different sigma, threshold and radius values. Experiments were developed on a database of 600 images from 100 individuals, with five image samples per individual for training and one image sample per individual for testing. The experimental results indicate that using Harris corner detector and Hamming distance using sliding window, recognition rate of 97.5% can be achieved.

Min Max Threshold Range (MMTR) based approach in palmprint authentication by sobel code method

Abstract Palmprint recognition is an effective biometric authentication method to automatically identify a person’s identity. Palmprint is rich in features like geometry features, line features, datum points, delta features and minutiae features. Several edge detection methods are available to extract line feature from the palmprint. In this paper, the hand image is pre-processed to get the desired Region of Interest (ROI)/palmprint. Multiscale Sobel Code operators of different orientations (0°, 45°, 90°, and 135°) are applied to the palmprint to extract Sobel-Palmprint features in different directions. The Sobel-Palmprint features extracted are stored in Sobel-Palmprint feature vector and matched using Hamming Distance similarity measurement method. In addition, a Min Max Threshold Range (MMTR) method is proposed that helps in increasing overall system accuracy by matching a person with multiple threshold values. In this technique, firstly the person is authenticated at global level using Reference threshold. Secondly, the person is authenticated at local level using range of Minimum and Maximum thresholds defined for a person. Generally, personal authentication is done using reference threshold but there are chances of false acceptance. So, by using the Minimum and Maximum Thresholds range of false accepted persons at personal level, a person is identified to be false accepted or genuinely accepted. MMTR is an effective technique to increase the accuracy of the palmprint authentication system by reducing the False Acceptance Rate (FAR). Experimental results indicate that the proposed method improves the False Acceptance Rate drastically.

Corner Detection using Phase Congruency Features

In todays automated world, it is always a great challenge to secure individuals personal information. Palmprint has come out to be one of the most secure and effective biometric methods to automatically identify a persons identity. It contains so many features like geometry features, principal line features, wrinkles, ridges, delta point features, texture features and points. Here, Phase Congruency Corner Detector (PCCD) method is proposed as a corner detection technique to extract palmprint features in the form of corners. Match by Correlation is used for similarity measurement. Experiments were developed on a database of 600 images from 100 individuals, with 5 image samples per individual for training and one image sample per individual for testing. The proposed approach of Phase Congruency Corner Detector when combined together with Match by Correlation has the ability to discriminate similar palmprints.

Multifactor Authentication Using a QR Code and a One-Time Password

In today’s world, communication, the sharing of information, and money transactions are all possible to conduct via the Internet, but it is important that it these things are done by the actual person. It is possible via several means that an intruder can access user information. As such, several precautionary measures have to be taken to avoid such instances. The purpose of this paper is to introduce the idea of a one-time password (OTP), which makes unauthorized access difficult for unauthorized users. A OTP can be implemented using smart cards, time-based tokens, and short message service, but hardware based methodologies require maintenance costs and can be misplaced Therefore, the quick response code technique and personal assurance message has been added along with the OTP authentication. Keywords—Authentication, One-Time Password, Personal Assurance Message, Quick Response Code

Min Max Threshold Range (MMTR) based approach in palmprint authentication by phase congruency features

Palmprint recognition is an effective biometric authentication method to automatically identify a persons identity. In this paper, phase congruency method is proposed to extract features from a palm-print image for authentication. The phase congruency is one of the promising methods to analyse the image as it is invariant to image contrast and therefore can extract reliable features under varying illumination conditions. In addition, a Min Max Threshold Range (MMTR) method is proposed that helps in increasing overall system accuracy by matching a person with multiple threshold values. In this technique, firstly the person is authenticated at global level using Reference threshold. Secondly, the person is authenticated at local level using range of Minimum and Maximum thresholds defined for a person. Generally, personal authentication is done using reference threshold but there are chances of false acceptance. So, by using the Minimum and Maximum Thresholds range of false accepted persons at personal level, a person is identified to be false accepted or genuinely accepted. MMTR is an effective technique to increase the accuracy of the palmprint authentication system by reducing the False Acceptance Rate (FAR). Hamming Distance similarity measurement method and Sliding Window approach is used to compare the similarity/dissimilarity between two feature vectors.

A Comprehensive Study of Passive Digital Image Forensics Techniques based on Intrinsic Fingerprints

Over the past decade digital images has become a very popular way to communicate, store and process information. With the rapid advancement and easy availability of technology, there is a flood of devices that are able to capture, store and create digital images. Over the past years image processing techniques have been developed that makes it really easy to tamper images. From journalism to social media edited images are appearing everywhere with increasing frequency. Authentication of images is very necessary as visual data effects what people perceive and believe. Digital image Forensics is an emerging field that uses intrinsic and extrinsic methods to authenticate digital images. Passive techniques extract and analyze inherent patterns introduced by various image processing steps and use these artifacts to associate the image with source device as well as to detect tampering of the digital images. This paper gives an overview of passive techniques of Digital Image Forensics which are based on intrinsic fingerprints inherent in digital images.

Sliding Window Based Fast Corner Matching Palmprint Authentication

Authentication time is the main and important part of the authentication system. Normally the response time should be fast but as the number of persons in the database increases, there is probability of more response time taken for authentication. The need of fast authentication system arises so that authentication time matching time is very less. This paper proposes a sliding window approach to make fast authentication system. The highlight of sliding window method is constant matching time, fast and can match translated images also. Several palmprint matching methods like match by correlation etc. are dependent upon the number of corners detected and so is the matching time. In sliding window method, matching time is constant as the numbers of matching operations are limited and the matching time is independent of the number of corners detected. The palmprint corner features extracted using two approaches Phase Congruency Corner Detector and Harris Corner Detector are binarized so that only useful information features is matched. The two approaches of Phase Congruency Corner Detector and Harris Corner Detector, when matched with hamming distance using sliding window can achieve recognition rate of 97.7% and 97.5% respectively.

Min Max Threshold Range (MMTR) Approach in Palmprint Recognition

Palmprint recognition is an effective biometric authentication method to automatically identify a person’s identity. The features in a palmprint include principal lines, wrinkles and ridges etc. All these features are of different length and thickness. It is not possible to analyse them in single resolution, so multi-resolution analysis technique is required. Here, Wavelet transform is proposed as a multi-resolution technique to extract these features. Euclidian distance is used for similarity measurement. In addition, a Min Max Threshold Range (MMTR) method is proposed that helps in increasing overall system accuracy by matching a person with multiple threshold values. In this technique, firstly the person is authenticated at global level using Reference threshold. Secondly, the person is authenticated at local level using range of Minimum and Maximum thresholds defined for a person. Generally, personal authentication is done using reference threshold but there are chances of false acceptance. So, by using the Minimum and Maximum Thresholds range of false accepted persons at personal level, a person is identified to be false accepted or genuinely accepted. MMTR is an effective technique to increase the accuracy of the palmprint authentication system by reducing the False Acceptance Rate (FAR). Experimental results indicate that the proposed method improves the False Acceptance Rate drastically.

Palmprint Authentication by Phase Congruency Features

The abstract should summarize the contents of the paper and should Palmprint recognition is an effective biometric authentication method to automatically identify a person’s identity. In this paper, phase congruency method is proposed to extract features from a palm-print image for authentication. The phase congruency is one of the promising methods to analyze the image as it is invariant to image contrast and therefore can extract reliable features under varying illumination conditions. The hand image is pre-processed to get the desired Region of Interest (ROI) / palmprint. The palmprint features are extracted by phase congruency method and are stored in feature vector. Euclidean Distance similarity measurement method is used to compare the similarity/dissimilarity between two feature vectors. Experiments were developed on a database of 600 images from 100 individuals, with five image samples per individual for training and one image sample per individual for testing.