Chetana Hegde

Heartbeat biometrics for human authentication

Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like the electrocardiogram (ECG) of a person is unique and secure. In this paper, we propose an authentication technique based on Radon transform. Here, ECG wave is considered as an image and Radon transform is applied on this image. Standardized Euclidean distance is applied on the Radon image to get a feature vector. Correlation coefficient between such two feature vectors is computed to authenticate a person. False Acceptance Ratio of the proposed system is found to be 2.19% and False Rejection Ratio is 0.128%. We have developed two more approaches based on statistical features of an ECG wave as our ground work. The result of proposed technique is compared with these two approaches and also with other state-of-the-art alternatives.

Authentication using Finger Knuckle Prints

Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like the finger knuckle print (FKP) of a person is unique and secure. Finger knuckle print is a novel biometric trait and is not explored much for real-time implementation. In this paper, three different algorithms have been proposed based on this trait. The first approach uses Radon transform for feature extraction. Two levels of security are provided here and are based on eigenvalues and the peak points of the Radon graph. In the second approach, Gabor wavelet transform is used for extracting the features. Again, two levels of security are provided based on magnitude values of Gabor wavelet and the peak points of Gabor wavelet graph. The third approach is intended to authenticate a person even if there is a damage in finger knuckle position due to injury. The FKP image is divided into modules and module-wise feature matching is done for authentication. Performance of these algorithms was found to be much better than very few existing works. Moreover, the algorithms are designed so as to implement in real-time system with minimal changes.

Human authentication using finger knuckle print

Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like Finger Knuckle Print (FKP) of a person is unique and secure. In this paper, we propose a human authentication system based on FKP image of a person. Depending on the security level required by an organization that implements the proposed system, we provide two modes of security viz. basic mode and advanced mode. The Radon Transform is applied on pre-processed FKP image and Eigen values are computed. For basic mode, we compute the correlation coefficient between the set of Eigen values stored in the database and that of input image to authenticate a person. For advanced level of security, we identify the peak points in Radon graph. The successive distances between those points are calculated and are stored in a vector. Now, the elements in distance vector stored in database and that of input image are compared. Such a match is considered to be success if the difference between two such elements is lesser than the threshold value. Now, the probability of success is computed. To authenticate a person in advanced mode, we use the correlation coefficient between Eigen values and the probability. For real time implementation, suitable GUI can be developed. The basic mode of security system is found to have FAR as 6.79% and FRR as 0.0517%. The advanced system has the FAR of about 1.55% and FRR as 1.02%.

Authentication of damaged hand vein patterns by modularization

As security is a major concern in present times, reliable authentication systems are in great demand. A biometric trait like the vascular pattern on the back of the hand of a person is unique and secure. A biometric system working on this principle often fails to authenticate a person either because of the varying hand posture or due to an injury altering the vein pattern. In this paper we propose an authentication system to overcome these disadvantages by modularizing the image and then comparing the features. This method of authentication reduces the False Rejection Ratio (FRR) and also False Acceptance Ratio (FAR) of the system.

FKP biometrics for human authentication using Gabor Wavelets

Automated security is one of the major concerns of modern times. Secure and reliable authentication systems are in great demand. A biometric trait like Finger Knuckle Print (FKP) of a person is unique and secure. In this paper, we propose a human authentication system based on FKP image of a person. We apply Gabor Wavelet on pre-processed FKP image. Then we identify the peak points in Gabor Wavelet graph. The successive distances between those points are calculated and are stored in a vector. Now, the elements in distance vector stored in database and that of input image are compared. Such a match is considered to be success if the difference between two such elements is lesser than the threshold value. Now, the probability of success is computed. The person is authenticated based on the value of computed probability. The proposed system has the FAR of about 1.24% and FRR as 1.11%.

Traffic signal time analysis and voice - based app for visually impaired pedestrians

Analysis of traffic signal and ensure the safety of visually impaired people is a major challenge in helping disabled people. In this paper, we propose development of an application software which can be easily installed on a mobile device equipped with a camera. This application opens a camera and captures the traffic timer display upon tapping the app-icon. The timer image is then processed to segment the digits in them to identify the actual time in the numeric form. The detected time is then converted as a voice message and played using the app. Thus, the visually impaired person can hear the message about the time left to turn on the pedestrian signal. Getting this alert message, he/she can safely cross the road. The accuracy of the proposed algorithm is found to be 100% as it detected all the digits in every instance of the timer display image.

Secured Human Authentication Using Finger-Vein Patterns

In any organization, providing a secured authentication system is a challenge. Here, we propose a secured authentication process using finger-vein patterns. Finger vein is a reliable biometric trait because of its distinctiveness and permanence properties. The proposed algorithm initially captures the finger-vein image and is preprocessed using Gaussian blur and morphological operations. Then features like number of corner points and the location of these corner points are extracted. The features fetched for an individual from database are compared against the extracted features. If the comparison satisfies predefined threshold value, then the authentication is successful. The simulation results of the proposed algorithm have produced the FAR as 2.78%, FRR as 0.09% and the overall performance as 99.96%.

Video shot boundary detection using statistical methods

Automatic video shot boundary detection is one of the major challenges involved in video piracy detection. In this paper, we propose a novel approach for detecting boundaries of the shots embedded within a video stream. Initially, video stream is segmented into different frames. Each frame will undergo preprocessing technique which will ease the further process of feature extraction. Statistical features like mean and standard deviation are computed for the frame differences. Local maxima of the vector of averages are considered to identify the outlier frame where there is a possible boundary of one shot. This method is found to be computationally simple and efficient. The accuracy of the proposed algorithm is found to be 98.9939%.