Form Of Biometric Identification Research Articles

Evaluating CNN Models for Gait Recognition: A Study on the CASIA-B Dataset

Gait recognition, a form of biometric identification, has garnered considerable interest for its ability to identify individuals from a distance. Yet, its effectiveness depends significantly on the precision of the underlying categorization models. This study thoroughly evaluates many Convolutional Neural Network (CNN) models to determine their effectiveness in properly identifying and categorizing gait patterns. We train and test several CNN architectures using advanced deep learning techniques on the widely known CASIA-B dataset, which is a benchmark dataset for gait recognition research. The analyses involve models like as VGG16, VGG19, NASNetLarge, NASNetMobile, EfficientNetB0, and Xception, each well-known for their effectiveness in picture categorization tasks. We evaluate the performance of these models by conducting thorough experiments and detailed analysis, focusing on accuracy, validation loss, and other pertinent metrics. The Xception model demonstrated the highest accuracy of 97.17% of the models assessed. This model frequently surpasses similar models, demonstrating its strength and effectiveness in precisely recognizing gait patterns. On the other hand, the accuracy of other models varied from 9.78% to 93.85%, demonstrating the variable levels of efficacy among various designs. The findings have significant implications for the creation and implementation of gait recognition systems. The high precision demonstrated by the Xception model highlights the promise of CNN-based methods in progressing gait identification technology. Our work highlights the significance of choosing suitable model architectures for achieving the best performance in gait detection tasks. In the future, research may focus on using larger datasets and exploring other CNN architectures to improve the accuracy and reliability of gait detection systems. Our goal is to enhance gait recognition technology to improve biometric identification for security, surveillance, and healthcare applications. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 17-26

Design and Analysis of an Expert System for the Detection and Recognition of Criminal Faces

The process of identifying a person using their facial traits is referred to as face recognition, and it is a form of biometric identification. The use of facial recognition might range from that of an entertainment tool to one of a security tool. Even while other forms of biometric identification, such as fingerprints and iris scans, are reliable, they require the active participation of an individual. As a result, criminals cannot rely on them as the most reliable means of verification. When a criminal database, which stores the individual details of a criminal, and facial recognition technology are brought together, it can identify a criminal who is depicted in an image or seen in a video feed. Not only does a criminal recognition system needs to have a high level of accuracy, but it also needs to be able to adapt to significant changes in lighting, occlusion, aging, expressions, and other factors. In this study, they were analyzed and compared with the many methods of face detection and face recognition, such as HAAR cascades, local binary patterns histogram, support vector machines, convolutional neural networks, and ResNet-34. These methods include a variety of different approaches to recognizing faces. An analysis of these strategies is also conducted and then put into practice to those that seem to be the most effective for the designed criminal recognition system. In addition to that, a variety of uses of this criminal recognition in the real world are also discussed.

A Literature Review of 2D Face Recognition Systems: Attacks and Defenses

Abstract:
 In a world where biometric identification and authentication is increasingly growing, it is critical for IT Security personnel to have an understanding of how these systems they use work, and weaknesses that lay with them. Face recognition is a very common form of biometric identification and authentication, where the face of the user is used to both identify and authenticate the user. This paper serves as a literature review for a preliminary understanding of face recognition systems. First the basics of how a facial recognition system works is reviewed. After this, selected articles demonstrating a variety of facial recognition spoofing attacks are summarized to demonstrate the attack surface. This is then followed by a review of studies conducted for defenses against these attacks, focusing primarily on the key concepts used to help identify spoofing attempts. This paper then concludes by summarizing the findings, and explaining the importance of understanding these different aspects of facial recognition from a business perspective.

IoT Based Portable Smart Lock

Security has been playing a key role in many of our places like home, offices, institution, suitcases, etc. In order to avoid intrusion from unauthorized person into these places a portable smart lock is proposed. Biometric systems and facial recognition have overtime served as robust security mechanisms in various domains. Fingerprint is most widely used form of biometric identification. Project builds an IoT based portable smart lock which can be opened through various means such as biometric fingerprint and facial recognition via mobile application using Wi-Fi or Bluetooth module. Database will be used to store the records of authorized person to unlock the lock. When an unauthorized person tries to unlock the lock a push message will be send to the owner of the lock and subsequently log of the same will be saved in the database .This database will be stored on web-server. Hence the lock will be unique combination of various aformentioned security features providing solution to problem of security.

"Brainprint" biometric id hits 100% accuracy [News

Psychologists and engineers at Binghamton University in New York say they've hit a milestone in the quest to use the unassailable inner workings of the mind as a form of biometric identification. They came up with an electroencephalograph system that proved 100 percent accurate at identifying individuals by the way their brains responded to a series of images. But EEG as a practical means of authentication is still far off.

A spatial domain scar removal strategy for fingerprint image enhancement

Fingerprints are the oldest and most widely used form of biometric identification. Many researchers have addressed the fingerprint classification problem and significant progress has been made in designing automatic fingerprint identification systems (AFIS) over the past two decades. However, some design factors such as lack of reliable minutia extraction algorithms, difficulty in quantitatively defining a reliable match between fingerprint images, poor image acquisition, low contrast images create bottlenecks in achieving the desired performance. Noticeable among them is the fact that digitally acquired fingerprint images are rarely of perfect quality to be used directly with AFIS; one important step is fingerprint enhancement. Conventional fingerprint enhancement methods, such as Gabor and anisotropic filters, do fill the holes and gaps in ridge lines but lack the necessary capability to tackle scar lines. For scar lines, an explicit filling process is proposed that is a mix of Fourier and spatial domain strategies. The proposed method is to make use of the Fourier domain directional field to trace an appropriate candidate for the scar pixels to be replaced with. The necessary components of the process are locating scars, estimating directional field, and the filling strategy. This process can act as front-end to the subsequent Gabor and anisotropic diffusion filtering. The simulation results for synthetic, as well as real fingerprints, show improved performance regarding better extraction of genuine minutia points.

Research focus: ‘body recognition’ and egocentric video ID

Australian researchers have uncovered a way to identify individuals without using facial, DNA or fingerprint recognition, claiming ‘body recognition’ could be a new form of biometric identification, reports IT News Australia.

Soft Biometrics; Human Identification Using Comparative Descriptions

Soft biometrics are a new form of biometric identification which use physical or behavioral traits that can be naturally described by humans. Unlike other biometric approaches, this allows identification based solely on verbal descriptions, bridging the semantic gap between biometrics and human description. To permit soft biometric identification the description must be accurate, yet conventional human descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe differences between subjects. This innovative approach has been shown to address many problems associated with absolute categorical labels-most critically, the descriptions contain more objective information and have increased discriminatory capabilities. Relative measurements of the subjects' traits can be inferred from comparative human descriptions using the Elo rating system. The resulting soft biometric signatures have been demonstrated to be robust and allow accurate recognition of subjects. Relative measurements can also be obtained from other forms of human representation. This is demonstrated using a support vector machine to determine relative measurements from gait biometric signatures-allowing retrieval of subjects from video footage by using human comparisons, bridging the semantic gap.

Fingerprint-Based Attendance Management System

In recent time, there has been high level of impersonation experienced on a daily basis in both private and public sectors, the ghost worker syndrome which has become a menace across all tiers of government, employers concerns over the levels of employee absence in their workforce and the difficulty in managing student attendance during lecture periods. Fingerprints are a form of biometric identification which is unique and does not change in one’s entire lifetime. This paper presents the attendance management system using fingerprint technology in a university environment. It consists of two processes namely; enrolment and authentication. During enrolment, the fingerprint of the user is captured and its unique features extracted and stored in a database along with the users identity as a template for the subject. The unique features called minutiae points were extracted using the Crossing Number (CN) method which extracts the ridge endings and bifurcations from the skeleton image by examining the local neighborhoods of each ridge pixel using a 3 x 3 window. During authentication, the fingerprint of the user is captured again and the extracted features compared with the template in the database to determine a match before attendance is made. The fingerprint-based attendance management system was implemented with Microsoft’s C# on the. NET framework and Microsoft’s Structured Query Language (SQL) Server 2005 as the backend. The experimental result shows that the developed system is highly efficient in the verification of users fingerprint with an accuracy level of 97.4%. The average execution time for the developed system was 4.29 seconds as against 18.48 seconds for the existing system. Moreover, the result shows a well secured and reliable system capable of preventing impersonation.

Handheld Imaging System for Contactless Tri-Modal Biometric Identification

Fingerprinting is the most widely used form of biometric identification because large databases are already available and law enforcement agencies have decades of experience with this technology. In addition, many fingerprinting units on the market are small in size, low power, and inexpensive. Common deficiencies carried by current fingerprinting technologies are distortions introduced when the finger touches the surface of the reader. The current study describes the development of a novel, handheld, contactless fingerprinting system which is capable of acquiring individual fingers with 180 degree view (first modality). Liveliness is examined by transmitted Infra-Red (IR) light (second modality), which allows the viewing of blood vessel networks and four-slap images and palm prints (third modality). This system is able to produce fingerprint images which are approximately 99% accurate representations of a finger's surface. This was verified by exam- ining a NIST certified aluminum test sample. As demonstrated in our study, this is compared to an average of 15% distortion of the distances between a finger's ridges when imaged with contact based systems. Our radial line-scan imaging allows for nail-to-nail acquisition of an individual fingerprint, where the format of the recorded image is analogous to an ink-rolled fingerprint. The system will securely and wirelessly transfers fingerprint images to any law enforcement center via built-in Wi-Fi connection.

The Use of Two Transform Methods in Fingerprints Recognition

Finger prints are the oldest and most widely used form of biometric identification. Despite the widespread use of fingerprints, there is little statistical theory on the uniqueness of fingerprint minutiae. Fingerprint matching is the process used to determine whether two sets of fingerprint ridge detail come from the same finger. There exist multiple algorithms that do fingerprint matching in many different ways. Some methods involve matching minutiae points between the two images, In this paper used median filter to enhance the images, and then use DCT (Discrete Cosine Transform) and FDCvT Via Wrapping to compute the feature extraction from the images. The Template Matching can be applied by finding the more similar values between the original image and the template.The proposed system includes two stages: first stage is implemented by taking individual natural fingerprint images with several positions and calculation of the features vector (Mean and standard deviation) by using FDCvT via Wrapping and DCT. The second stage is implemented by taking several samples of new fingerprint images for testing the work. The results show that the fingerprints Recognition rate by the (FDCvT via Wrapping and DCT) achieves better recognition rate (84%).

Finger Print Recognition: Survey of Minutiae and Gabor Filtering Approach

Fingerprints are the most popular and studied biometrics features. Their stability and uniqueness make the fingerprint identification system extremely reliable and useful for security applications. Fingerprints are the oldest and most widely used form of biometric identification. Everyone is known to have unique, immutable fingerprints. This paper deals with the issue of selection o fan optimal algorithm for fingerprint matching in order to design a system that matches required specifications in performance and accuracy. Two approaches have been discussed in this paper based on minutiae located in a fingerprint and based on gabor filter which is used to matching the fingerprint. General Terms Fingerprint recognition, minutiae technique, gabor filter technique.

Both Hands’ Fingers’ Angle Calculation from Live Video

In the last few years gesture recognition and gesture-based human computer interaction has gained a significant amount of popularity amongst researchers all over the world. It has a number of applications ranging from security to entertainment. Gesture recognition is a form of biometric identification that relies on the data acquired from the gesture depicted by an individual. This data, which can be either two-dimensional or three-dimensional, is compared against a database of individuals or is compared with respective thresholds based on the way of solving the riddle. In this paper, a novel method for angle calculation of both hands’ bended fingers is discussed and its application to a robotic hand control is presented. For the first time, such a study has been conducted in the area of natural computing for calculating angles without using any wired equipment, colors, marker or any device. The system deploys a simple camera and captures images. The pre-processing and segmentation of the region of interest is performed in a HSV color space and a binary format respectively. The technique presented in this paper requires no training for the user to perform the task.

Warping-Based Offline Signature Recognition

Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper, we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a 1-D optimization process that is possible when a 1-D parametrization of the curves is known. In this paper, we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of 1-D parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first-order ordinary differential equations. The proposed approach is, therefore, capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use were collected from real-world documents, spanning a period of several decades.