Latent Palmprints Research Articles

Enhancing latent palmprints using frequency domain analysis

Latent palmprints are integral to crime scene investigations, constituting a significant portion of encountered prints. They often suffer from poor ridge impressions, noise, and pronounced creases, setting them apart from other palmprint types. While progressive enhancement techniques are widely used for fingerprints, palmprints with numerous thick creases and larger sizes benefit more from region-growing techniques. Frequency domain-based palmprint enhancement excels in separating creases from ridges and reshaping ridge structures accurately. The key challenge lies in identifying suitable initial blocks for both region-growing and iterative enhancement techniques. Existing frequency domain-based quality maps, primarily designed for fingerprints, exhibit limited performance when applied to palmprints, especially latent ones. To address these issues, this paper introduces a new approach that combines region-growing and frequency domain-based enhancement techniques to improve latent palmprints. Our method leverages high-quality blocks, employs the orientation field obtained in the frequency domain to correct possible orientation errors in starting blocks, and utilizes varying weights to enhance all block types effectively. The experimental results indicate that the proposed approach surpasses the existing state-of-the-art techniques in terms of recognition accuracy.

A novel indexing algorithm for latent palmprints leveraging minutiae and orientation field

Latent palmprints represent crucial forensic evidence in criminal investigations, necessitating their storage in governmental databases. The identification of corresponding palmprints within large-scale databases using an automated palmprint identification system (APIS) is time-consuming and computationally intensive. To address this challenge, this paper introduces an innovative approach: delineating the region of interest (ROI) for palmprint segmentation and presenting a novel indexing algorithm founded on minutiae and the orientation field (OF). Additionally, a novel feature vector is proposed, leveraging minutiae triplets and ellipse properties, marking the pioneering algorithm to consider minutiae importance in palmprint indexing. Significantly, an improved version of an existing palmprint indexing algorithm tailored for latent palmprints is introduced. The study demonstrates the indexing and retrieval of both our feature vectors and those obtained by the improved palmprint indexing algorithm, using two clustering algorithms and locality-sensitive hashing (LSH). The method's robustness is evaluated across three diverse databases with extensive palmprint records. The experimental results underscore the superior performance of our approach compared to current state-of-the-art algorithms.

IMPLEMENTATION OF ACCURATE PERSONAL IDENTIFICATION BY USING PALM PRINT IMAGE PROCESSING

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses highresolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. Among various biometrics technologies, palm-print identification has received much attention because of its good performance. Combining the left and right palm-print images to perform multi-biometrics is easy to implement and can obtain better results. Existing systems deployed Line Based Method, Coding Based Method, Subspace Based Methods, Representation Based Method, SIFT Based Method. This work integrated three kinds of scores generated from the left and right palm-print images to perform matching score-level fusion. The first two kinds of scores were, respectively, generated from the left and right palm-print images and can be obtained by any palm-print identification method, whereas the third kind of score was obtained using a specialized algorithm proposed in this paper. As the proposed algorithm carefully takes the nature of the left and right palm-print images into account, it can properly exploit the similarity of the left and right palm-prints of the same subject. Moreover, the proposed weighted fusion scheme allowed perfect identification performance to be obtained in comparison with previous palm-print identification methods.

A secure and robust indexing algorithm for distorted fingerprints and latent palmprints

Nowadays, both fingerprint and palmprint databases are massive and whose size has exceeded millions. Therefore, applying a filtering technique, such as indexing, is vital for automatic fingerprint/palmprint identification systems. Geometric distortion present in fingerprints and the creases in palmprints have a significant drop in recognition accuracy. Moreover, protecting an enrollment template is an important and challenging issue today. Template protection is a technique to convert an unprotected enrollment biometric template to a protected biometric template and is used to prevent the access of illegal users and attackers to the enrolled biometric template. Hence, in this paper, we propose a new feature called the middle of the triangle’s side (MTS) derived from each minutiae pair of a triangle-based representation to mitigate the negative effect of the geometric distortion and creases. Furthermore, they can be used as a feature transformation to protect templates. For computing MTSs, we first estimate the quality of input images and then extract reliable minutiae from the input images. After that, we apply the Delaunay triangulation of order k to minutiae for obtaining a triangle-based representation. Then, we calculate the median of sides of each triangle for extracting MTSs. To obtain the direction of each MTS, we use the direction difference between each minutiae pair placed in both vertices of the triangle’s side. This makes MTSs robust against fingerprint and palmprint rotation. Afterward, we obtain new triangles by connecting the MTSs of each triangle and weight feature vectors based on the quality of minutiae, and generate indices. Finally, we propose a new feature vector for triangles to increase the recognition accuracy. Experimental results on two public fingerprint databases containing distorted fingerprints, and two public palmprint databases containing latent prints, show that MTSs are more robust than minutiae against geometric distortion. Also, MTSs are secure, and its reason is that minutiae direction and location are changed. In addition, the number of MTSs is more than minutiae, making them more appropriate for prints with a low number of minutiae. Our experimental results show that MTSs and the proposed indexing algorithm can obtain good results for distorted fingerprints and latent palmprints.

Identification of Covid’19 Vaccinator by Deep Learning Approach Using Contactless Palmprints

The invention of the first vaccine has also raised several anti-vaccination views among people. Vaccine reluctance may be exacerbated by the growing reliance on social media, which is considered as a source of health information. During this COVID'19 scenario, the verification of non-vaccinators via the use of biometric characteristics has received greater attention, especially in areas such as vaccination monitoring and other emergency medical services, among other things. The traditional digital camera utilizes the middle-resolution images for commercial applications in a regulated or contact-based environment with user participation, while the latter uses high-resolution latent palmprints. This research study attempts to utilize convolutional neural networks (CNN) for the first time to perform contactless recognition. To identify the COVID '19 vaccine using the CNN technique, this research work has used the contactless palmprint method. Further, this research study utilizes the PalmNet structure of convolutional neural network to resolve the issue. First, the ROI region of the palmprint was extracted from the input picture based on the geometric form of the print. After image registration, the ROI region is sent into a convolutional neural network as an input. The softmax activation function is then used to train the network so that it can choose the optimal learning rate and super parameters for the given learning scenario. The neural networks of the deep learning platform were then compared and summarized.

Palmprint Recognition in Uncontrolled and Unco-operative Environment

Abstract: On-line palmprint recognition and latent palmprint identification unit two branches of palmprint studies. The previous uses middle-resolution footage collected by a camera in an exceedingly} very well-controlled or contact-based surroundings with user cooperation for industrial applications and so the latter uses high resolution latent palmprints collected in crime scenes for rhetorical investigation. However, these two branches do not cowl some palmprint footage that have the potential for rhetorical investigation. Attributable to the prevalence of smartphone and shopper camera, further proof is at intervals the variability of digital footage taken in uncontrolled and uncooperative surroundings. However, their palms area unit typically noticeable. To visualize palmprint identification on footage collected in uncontrolled and uncooperative surroundings, a novel palmprint info is established Associate in nursing AN end-to-end deep learning rule is projected. The new data named NTU Palmprints from the net (NTU-PI-v1) contains 7881 footage from 2035 palms collected from the net. The projected rule consists of Associate in Nursing alignment network and a feature extraction network and is end-to-end trainable. The projected rule is compared with the progressive on-line palmprint recognition ways that and evaluated on three public contactless palmprint infos, IITD, CASIA, and PolyU and a couple of new databases, NTU-PI-v1 and NTU contactless palmprint info. The experimental results showed that the projected rule outperforms the current palmprint recognition ways that. Keywords: Biometrics, criminal and victim identification, forensics, palmprint recognition

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.

Latent Palm Print Matching Based on Minutiae Features for Forensic Applications

In forensic application, palm print recognition receives significant attention because of the developing live scan palm print technology. In most forensic applications, the critical evidence obtained is those of latent palm prints. About 30% of the latent obtained from the crime scenes are of palms. Palm prints has large area of foreground, which consists of 1000’s of minutiae points. Hence latent palm print matching requires development of novel strategies. Here we design a palm print matching strategy which is based on minutiae clustering. The minutiae match propagation is also considered for the minutiae correspondence. Minutiae clusters are formed based on the local minutiae features. Each cluster contains minutiae that contain similar local features. The correspondence between two palm print is done within the clusters. Mated minutiae pairs are considered by the Minutiae match propagation algorithm to determine the correspondence between the query palm print and the full palm print. Starting with the initial minutiae pair, the algorithm checks for further matches with the full palm print image

Role of Latent Palm Prints Present on Documents in Establishment of Individuality

Role of Latent Palm Prints Present on Documents in Establishment of Individuality

Role of Latent Palm Prints Present on Documents in Establishment of Individuality

Role of Latent Palm Prints Present on Documents in Establishment of Individuality

Latent Palm Prints- an Appraisal of Concealed individualize Evidence and Its Aspect in Forensic Investigation

Latent Palm Prints- an Appraisal of Concealed individualize Evidence and Its Aspect in Forensic Investigation

Determination of Sex and Stature from Latent Palm Prints Present on Documents in Egyptian Population Sample

Determination of Sex and Stature from Latent Palm Prints Present on Documents in Egyptian Population Sample

Determination of Sex from the Latent Palm Prints Present on Documents

In the field of forensic science, to determine the sex of suspect from latent prints found at crime scene is often suspicious. Identification of an individual's sex is being possible from the latent palm prints present on documents. To determine the sex from the latent palm print can be done with minutiae details of developed prints from the documents, which will be helping for the investigators in nabbing the suspects. For this study, 60 samples including male and female from villages of Uttar Pradesh, India were taken for the determination of sex of an individual through the development of latent palm prints present on documents. The Obtained 12 parameters (radius of curvatures, Angle of curvature from line of writing, Inter distance between centre of curvatures, and Distance of centre of curvatures from line of writing), their mean values and standard deviations from this study not only gives the significant of sex at 95% confidence level of difference but also considered statistically significant (p<0.05). On behalf of which determination of sex can be done. It also can increase the evidential value of such lateral palm prints. These evidences can be helpful for reducing the pool of potential suspects in investigations, and also can be placed.

A Coarse to Fine Minutiae-Based Latent Palmprint Matching

With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high-resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature-based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4 percent, which is significantly higher than the 60.8 percent identification accuracy of a state-of-the-art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141 ms for genuine match and 50 ms for impostor match, on a Windows XP desktop system with 2.2-GHz CPU and 1.00-GB RAM. The computation time of our algorithm is an order of magnitude faster than a previously published state-of-the-art-algorithm.

Morphometry of latent palmprints as a function of time

In many crimes, the elapsed time between production and collecting fingermark traces is crucial. and a method able to detect the aging of latent prints would represent an improvement in forensic procedures. Considering that as the latent print gets older, substantial changes in the relative proportion of individual components secreted by skin glands could affect the morphology of ridges, morphometry could be a potential tool to assess the aging of latent fingermarks. Then, considering the very limited research in the field, the present work aims to evaluate the morphometry of latent palmprint ridges, as a function of time, in order to identify an aging pattern. The latent marks were deposited by 20 donors on glass microscope slides considering pressure and contact angle, and then were maintained under controlled environmental conditions. The morphometric study was conducted on marks developed with magnetic powder in 7 different time intervals after deposition (0, 5, 10, 15, 20, 25 or 30 days); 60 ridges were evaluated for each developed mark. The results showed that: 1) the method for the replacement and mixing of skin secretions on the palm was appropriate to ensure reproducibility of latent prints, and 2) considering the studied group, there was a time-dependent reduction in the width of ridges and on the percentage of visible ridges over 30 days. Results suggest the possibility of using the morphometric method to determine an aging profile of latent palmprints on glass surface, aiming for forensic purposes.

Latent Palmprint Matching

The evidential value of palmprints in forensic applications is clear as about 30 percent of the latents recovered from crime scenes are from palms. While biometric systems for palmprint-based personal authentication in access control type of applications have been developed, they mostly deal with low-resolution (about 100 ppi) palmprints and only perform full-to-full palmprint matching. We propose a latent-to-full palmprint matching system that is needed in forensic applications. Our system deals with palmprints captured at 500 ppi (the current standard in forensic applications) or higher resolution and uses minutiae as features to be compatible with the methodology used by latent experts. Latent palmprint matching is a challenging problem because latent prints lifted at crime scenes are of poor image quality, cover only a small area of the palm, and have a complex background. Other difficulties include a large number of minutiae in full prints (about 10 times as many as fingerprints), and the presence of many creases in latents and full prints. A robust algorithm to reliably estimate the local ridge direction and frequency in palmprints is developed. This facilitates the extraction of ridge and minutiae features even in poor quality palmprints. A fixed-length minutia descriptor, MinutiaCode, is utilized to capture distinctive information around each minutia and an alignment-based minutiae matching algorithm is used to match two palmprints. Two sets of partial palmprints (150 live-scan partial palmprints and 100 latent palmprints) are matched to a background database of 10,200 full palmprints to test the proposed system. Despite the inherent difficulty of latent-to-full palmprint matching, rank-1 recognition rates of 78.7 and 69 percent, respectively, were achieved in searching live-scan partial palmprints and latent palmprints against the background database.