Biometric Authentication Algorithm Research Articles

Facial Detection and Recognition System Using Python

Most of us are already aware of the fact that each time we upload a photograph in social media, the platform uses biometric authentication algorithms. But not many of us are aware that it can be made by a language as popular as python, which is both simple to learn and efficient enough to build major algorithms, most of which are a part of the field of deep learning. The language also has a variety of libraries that serves as a pool of resources to perform some exciting tasks like graph plotting, AI based algorithms etc. To aid us with the necessary tools to perform this kind of detection, we have used the very famous OpenCV library. This project helped us brush up our skills and expand our knowledge in the field of python. The target is to train the machine to determine, identify and process images like humans. This paper shows our study about various ways by which this can be done and the methodology opted by us.

A Biometric Authentication Technique Using Smartphone Fingertip Photoplethysmography Signals

Biometric Authentication (BA) is the process by which physiological measurements can be used to identify a specific person. BA has been implemented in various applications such as financial transactions, data privacy, and access control. Photoplethysmography (PPG) signals can be acquired from devices such as smartphones, smartwatches, or web cameras, which allow biometric signals to be readily captured. In this paper, we developed a BA classifier method for smart devices, which first reduces motion and noise artifacts (MNAs) in raw PPG signals, and then identifies subjects using the MNA-reduced PPG signals. Specifically, our BA classifier algorithm adopts an ensemble bagged trees (EBT) classifier with 16 PPG signal features from time and frequency domains. In the BA context, false positive (FP) and false negative (FN) rates are very important measurements to be considered. Acceptable FP and FN values were obtained. Experimental results show that our EBT-based BA algorithm achieved accuracy levels of 98.0% and 95.0%. Also, equal error rates (EER) measurements accomplished acceptable values of 2.42% and 5.90%.

Immersive online biometric authentication algorithm for online guiding based on face recognition and cloud-based mobile edge computing

Immersive online biometric authentication algorithm for online guiding based on face recognition and cloud-based mobile edge computing

A Dynamic Biometric Authentication Algorithm for Near-Infrared Palm Vascular Patterns

In this paper we apply a novel approach to near-infrared subcutaneous palm vascular pattern authentication. The proposed method relies on a recursive algorithm based on a positive linear dynamical system whose evolution depends on the two matrices representing the vein patterns to be compared. The output of the system reaches a high value when a good matching between the two matrices is observed, otherwise it converges rapidly to zero, even in presence of noise. With respect to another algorithm we recently introduced, this approach achieves not only a better authentication performance but also a drastic reduction in terms of computation time. These improvements are demonstrated by means of extensive experiments conducted on challenging datasets.

Multimodal Biometric Authentication Algorithm Using Iris, Palm Print, Face and Signature with Encoded DWT

A multimodal biometric system is applied to recognize individuals as authentication, identification and verification for claimed identity. Multimodal biometrics increases the security level accuracy, spoof of attacks, noise in collected data, intra-class variations, inter-class variations, non universality etc. In this paper a multi modal biometric algorithm is designed by integrating iris, palm print, face and signature based on encoded discrete wavelet transform for image analysis and authentication. Multi level wavelet based fusion approach is applied, integrated and encoded into single composite image for matching decision. It reduces the memory size, increases the recognition accuracy and ERR using multimodal biometric approach when compared to individual biometric traits. The complexity of fusion and the reconstruction algorithm is suitable for many real time applications.

ECG Authentication for Mobile Devices

Traditional mobile login methods, like numerical or graphical passwords, are vulnerable to passive attacks. It is common for intruders to gain access to personal information of their victims by watching them enter their passwords into their mobile screens from a close proximity. With this in mind, a mobile biometric authentication algorithm based on electrocardiogram (ECG) is proposed. With this algorithm, the user will only need to touch two ECG electrodes (lead I) of the mobile device to gain access. The algorithm was tested with a cell phone case heart monitor in a controlled laboratory experiment at different times and conditions with ten subjects and also with 73 records obtained from the Physionet database. The obtained results reveal that our algorithm has 1.41% false acceptance rate and 81.82% true acceptance rate with 4 s of signal acquisition. To the best of our knowledge, this is the first approach on mobile authentication that uses ECG biometric signals and it shows a promising future for this technology. Nonetheless, further improvements are still needed to optimize accuracy while maintaining a short acquisition time for authentication.

Neural Network Based Normalized Fusion Approaches for Optimized Multimodal Biometric Authentication Algorithm

A multimodal biometric system is applied to recognize individuals for authentication using neural networks. In this paper multimodal biometric algorithm is designed by integrating iris, finger vein, palm print and face biometric traits. Normalized score level fusion approach is applied and optimized, encoded for matching decision. It is a multilevel wavelet, phase based fusion algorithm. This robust multimodal biometric algorithm increases the security level, accuracy, reduces memory size and equal error rate and eliminates unimodal biometric algorithm vulnerabilities.