Touchless Fingerprint Recognition Research Articles

SMART SENSOR BASED TOUCHLESS FINGERPRINT SYSTEM

his paper introduces a touchless fingerprint recognition system, offering a hygienic alternative to conventional touch-based systems. The primary objective is to develop and evaluate a comprehensive solution that addresses hygiene-related concerns and mitigates the risks associated with disease transmission, particularly in the context of the ongoing Covid-19 pandemic. The proposed system utilizes a state-of-the-art touchless sensor to capture intricate fingerprint images, subsequently processing, storing, and retrieving them for matching purposes. By eliminating the need for physical contact, the system effectively tackles issues related to inconsistent pressure and prolonged collection times. The touchless fingerprint technology showcased in this system holds great promise for diverse applications that demand secure and hygienic fingerprint recognition. The proposed method overcomes the limitations inherent in touch-based systems, making it well-suited for environments where health and safety considerations are paramount. The integration of this touchless technology not only enhances security but also contributes significantly to public health by reducing the risk of cross-contamination in shared touchpoints. As the world navigates the challenges posed by the pandemic, the adoption of touchless fingerprint recognition systems represents a crucial step toward creating safer and more hygienic environments.

GAN-Based Blur Restoration for Finger Wrinkle Biometrics System

Existing methods for iris, fingerprint, and 3D face recognition in mobile devices have constraints in terms of price and size owing to their use of additional cameras, lighting, and sensors. Additionally, visible light, camera-based 2D face recognition, palm print recognition, touchless fingerprint recognition, and finger knuckle print recognition are difficult to be used in mobile devices due to limitations in recognition performance and user inconvenience. In response to these problems, studies have been conducted on finger wrinkle recognition in mobile devices; however, image quality is often reduced by motion blurring caused by the movement of the camera or the user’s finger, thereby reducing recognition performance. This study proposes a method for restoring and recognizing motion-blurred finger wrinkle images based on a generative adversarial network and deep convolutional neural network. Experiments were performed using two types of finger wrinkle databases, which were custom-made from images of 33 people captured by smart phone cameras (Dongguk mobile finger wrinkle database versions 1 and 2, denoted as DMFW-DB1 and DMFW-DB2, respectively). The results demonstrated high restoration and recognition performance in comparison with the state-of-the-art methods.

Towards smartphone-based touchless fingerprint recognition

The widely used conventional touch-based fingerprint identification system has drawbacks like the elastic deformation due to nonuniform pressure, fingerprints collection time and hygiene. To overcome these drawbacks, recently the touchless fingerprint technology is gaining popularity and various touchless fingerprint acquisition solutions have been proposed. Nowadays due to the wide use of the smartphone in various biometric applications, smartphone-based touchless fingerprint systems using an embedded camera have been proposed in the literature. These touchless fingerprint images are very different from conventional ink-based and live-scan fingerprints. Due to varying contrast, illumination and magnification, the existing touch-based fingerprint matchers do not perform well while extracting reliable minutiae features. A touchless fingerprint recognition system using a smartphone is proposed in this paper, which incorporates a novel monogenic-wavelet-based algorithm for enhancement of touchless fingerprints using phase congruency features. For the comparative performance analysis of our system, we created a new touchless fingerprint database using the developed android app and this is publicly made available along with its corresponding live-scan images for further research. The experimental results in both verification and identification mode on this database are obtained using three widely used touch-based fingerprint matchers. The results show a significant improvement in Rank-1 accuracy and equal error rate (EER) achieved using the proposed system and the results are comparable to that of the touch-based system.

Empirical study on touchless fingerprint recognition using a phone camera

Existing methods for touchless fingerprint recognition using a phone camera have the difficulties to quickly verify and extract fingerprint images with high recognition accuracy. It is because specialized techniques for touchless fingerprint images were not provided for them. To overcome these difficulties, three methods are proposed. First, a finger-shaped guide is displayed on the phone screen to enable the user to position their fingers correctly and to focus the camera on the position to quickly produce a good fingerprint image. Second, the profile line tests at selected points on the guide are proposed to quickly determine whether the user’s fingers are properly placed according to the guide shape and to simplify the segmentation of fingerprint image regions. Third, a existing local structure-based matching method is improved for touchless fingerprint images, and a score-level fusion of the three finger matchings is performed to ensure high recognition accuracy. Experiments were performed using our custom-built database, where touchless fingerprint images were collected using a 13-MP mobile camera in various indoor and outdoor environments. Resulting error rates were between 0.50% and 1.04% depending on whether the image was captured indoors or outdoors.

Toward Unconstrained Fingerprint Recognition: A Fully Touchless 3-D System Based on Two Views on the Move

Touchless fingerprint recognition systems do not require contact of the finger with any acquisition surface and thus provide an increased level of hygiene, usability, and user acceptability of fingerprint-based biometric technologies. The most accurate touchless approaches compute 3-D models of the fingertip. However, a relevant drawback of these systems is that they usually require constrained and highly cooperative acquisition methods. We present a novel, fully touchless fingerprint recognition system based on the computation of 3-D models. It adopts an innovative and less-constrained acquisition setup compared with other previously reported 3-D systems, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving. To compensate for possible differences in finger placement, we propose novel algorithms for computing 3-D models of the shape of a finger. Moreover, we present a new matching strategy based on the computation of multiple touch-compatible images. We evaluated different aspects of the biometric system: acceptability, usability, recognition performance, robustness to environmental conditions and finger misplacements, and compatibility and interoperability with touch-based technologies. The proposed system proved to be more acceptable and usable than touch-based techniques. Moreover, the system displayed satisfactory accuracy, achieving an equal error rate of 0.06% on a dataset of 2368 samples acquired in a single session and 0.22% on a dataset of 2368 samples acquired over the course of one year. The system was also robust to environmental conditions and to a wide range of finger rotations. The compatibility and interoperability with touch-based technologies was greater or comparable to those reported in public tests using commercial touchless devices.

Touch-less Fingerprint Recognition UsingSVM and GMM: A Comparative Study

Fingerprint biometrics provides identity verification with a strong degree of confidence over past few decades. Present work focuses on special branch of fingerprint recognition namely, touch-less fingerprint recognition which has fundamental advantage in terms of better hygiene, safety and latent fingerprints over traditional touch-based systems. Such systems find applications in numerous fields such as secure access to laptops and computer systems, cellular phones, banking, ATMs etc. Although lot of work have been done on touch-less fingerprint recognition systems, still people are looking for more accuracy. The proposed touch-less fingerprint recognition system opt digital camera as the device to acquire the fingerprint image and it consists of three main steps: Pre-processing, Feature extraction and Verification. Current work presents a comparative performance evaluation of two widely used classifiers: Support Vector Machine (SVM) and Gaussian Mixture Model (GMM). Experimental results illustrates that GMM shows slightly better accuracy than SVM.

Distal-Interphalangeal-Crease-Based User Authentication System

Touchless-based fingerprint recognition technology is thought to be an alternative to touch-based systems to solve problems of hygienic, latent fingerprints, and maintenance. However, there are few studies about touchless fingerprint recognition systems due to the lack of a large database and the intrinsic drawback of low ridge-valley contrast of touchless fingerprint images. This paper proposes an end-to-end solution for user authentication systems based on touchless fingerprint images in which a multiview strategy is adopted to collect images and the robust fingerprint feature of touchless image is extracted for matching with high recognition accuracy. More specifically, a touchless multiview fingerprint capture device is designed to generate three views of raw images followed by preprocessing steps including region of interest (ROI) extraction and image correction. The distal interphalangeal crease (DIP)-based feature is then extracted and matched to recognize the human's identity in which part selection is introduced to improve matching efficiency. Experiments are conducted on two sessions of touchless multiview fingerprint image database with 541 fingers acquired about two weeks apart. An EER of ~ 1.7% can be achieved by using the proposed DIP-based feature, which is much better than touchless fingerprint recognition by using scale invariant feature transformation (SIFT) and minutiae features. The given fusion results show that it is effective to combine the DIP-based feature, minutiae, and SIFT feature for touchless fingerprint recognition systems. The EER is as low as ~ 0.5%.

Touch-less Fingerprint Analysis — A Review and Comparison

Touch-less fingerprint recognition system is a reliable alternative to conventional touch-based fingerprint recognition system. Touch-less system is different from conventional system in the sense that they make use of digital camera to acquire the fingerprint image where as conventional system uses live-acquisition techniques. The conventional fingerprint systems are simple but they suffer from various problems such as hygienic, maintenance and latent fingerprints. In this paper we present a review of touch-less fingerprint recognition systems that use digital camera. We present some challenging problems that occur while developing the touch-less system. These problems are low contrast between the ridge and the valley pattern on fingerprint image, non-uniform lighting, motion blurriness and defocus, due to less depth of field of digital camera. The touch-less fingerprint recognition system can be divided into three main modules: preprocessing, feature extraction and matching. Preprocessing is an important step prior to fingerprint feature extraction and matching. In this paper we put our more emphasis on preprocessing so that the drawbacks stated earlier can be removed. Further preprocessing is divided into four parts: first is normalization, second is fingerprint Segmentation, third is fingerprint enhancement and last is the core point detection. Feature extraction can be done by Gabor filter or by minutia extraction and the matching can be done by Support Vector Machine or Principal Component Analysis and three distance method.