Biometric Validation Research Articles

Hybrid-Blockchain-Based Electronic Voting Machine System Embedded with Deepface, Sharding, and Post-Quantum Techniques

The integrity of democratic processes relies on secure and reliable election systems, yet achieving this reliability is challenging. This paper introduces the Post-Quantum Secured Multiparty Computed Hierarchical Authoritative Consensus Blockchain (PQMPCHAC-Bchain), a novel e-voting system designed to overcome the limitations of current Biometric Electronic Voting Machine (EVM) systems, which suffer from trust issues due to closed-source designs, cyber vulnerabilities, and regulatory concerns. Our primary objective is to develop a robust, scalable, and secure e-voting framework that enhances transparency and trust in electoral outcomes. Key contributions include integrating hierarchical authorization and access control with a novel consensus mechanism for proper electoral governance. We implement blockchain sharding techniques to improve scalability and propose a multiparty computed token generation system to prevent fraudulent voting and secure voter privacy. Post-quantum cryptography is incorporated to safeguard against potential quantum computing threats, future-proofing the system. Additionally, we enhance authentication through a deep learning-based face verification model for biometric validation. Our performance analysis indicates that the PQMPCHAC-Bchain e-voting system offers a promising solution for secure elections. By addressing critical aspects of security, scalability, and trust, our proposed system aims to advance the field of electronic voting. This research contributes to ongoing efforts to strengthen the integrity of democratic processes through technological innovation.

Liveness Identity Verification for Face Anti-Spoofing in Biometric Validation using Recurrent Neural Network

Face anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person's face. It has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks. However, most previous approaches still suffer from diverse types of spoofing attacks, which are hardly covered by the limited number of training datasets, and thus they often show the poor accuracy when unseen samples are given for the test. To address this problem, a novel method is proposed based on liveness identity verification for face anti-spoofing in biometric validation using the Recurrent Neural Network (RNN). Keyword : Biometric Validation, Face Anti-Spoofing Identification, Face Liveness Detection, Face Recognition, Lightweight CNN, Machine Learning, RNN.

Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.

FaceHack: Attacking Facial Recognition Systems Using Malicious Facial Characteristics

Recent advances in machine learning have opened up new avenues for its extensive use in real-world applications. Facial recognition, specifically, is used from simple friend suggestions in social-media platforms to critical security applications for biometric validation in automated border control at airports. Considering these scenarios, security vulnerabilities of such facial recognition systems pose serious threats with severe outcomes. Recent work demonstrated that Deep Neural Networks (DNNs), typically used in facial recognition systems, are susceptible to backdoor attacks; in other words, the DNNs turn malicious in the presence of a unique trigger. Detection mechanisms have focused on identifying these distinct trigger-based outliers statistically or through reconstructing them. In this work, we propose the use of facial characteristics as triggers to backdoored facial recognition systems. Additionally, we demonstrate that these attacks can be realised on real-time facial recognition systems. Depending on the attack scenario, the changes in the facial attributes may be embedded artificially using social-media filters or introduced naturally through facial muscle movements. We evaluate the success of the attack and validate that it does not interfere with the performance criteria of the model. We also substantiate that our triggers are undetectable by thoroughly testing them on state-of-the-art defense and detection mechanisms.

Towards Facial Biometrics for ID Document Validation in Mobile Devices

Various modern security systems follow a tendency to simplify the usage of the existing biometric recognition solutions and embed them into ubiquitous portable devices. In this work, we continue the investigation and development of our method for securing identification documents. The original facial biometric template, which is extracted from the trusted frontal face image, is stored on the identification document in a secured personalized machine-readable code. Such document is protected from face photo manipulation and may be validated with an offline mobile application. We apply automatic methods of compressing the developed face descriptors to make the biometric validation system more suitable for mobile applications. As an additional contribution, we introduce several print-capture datasets that may be used for training and evaluating similar systems for mobile identification and travel documents validation.

A Human Gait Recognition Against Information Theft in Smartphone using Residual Convolutional Neural Network

The genuine user of the smartphone is identified and information theft is prevented by continuous authentication, which is one of the most emerging features in biometrics application. A person is recognized by analysing the physiological or behavioural attributes is defined as biometrics. The physiological qualities include iris acknowledgment, impression of finger, palm and face geometry are used in the biometric validation frameworks. In the existing entry-point authentication techniques, a confidential information is lost because of internal attacks, while identifying the genuine user of the smartphone. Therefore, a biometric validation framework is designed in this research study to differentiate an authorized user by recognizing the gait. In order to identify the unauthorized smartphone access, a human gait recognition is carried out by implementing a Residual Convolutional Neural Network (RCNN) approach. A personal information of end user in smartphone is secured and presented a better solution from unauthorized access by proposed architecture. The performance of RCNN method is compared with the existing Deep Neural Network (DNN) in terms of classification accuracy. The simulation results showed that the RCNN method achieved 98.15% accuracy, where DNN achieved 95.67% accuracy on OU-ISIR dataset.

A Review on Biometric Technology

Biometric Technology has turned out to be a popular area of research in computer vision and one of the most successful applications for identifying humans by capturing and analysing the sole feature or characteristic of individual which is possessed by them and involves their Physical and Behavioral characteristics. For the individual validation and authentication the biometric system has this responsibility. Biometric Technology started from the fingerprints recognition and later on improvements were done in it to make it more secure which involves the face recognition and iris Recognition. Almost both of them are available and regarded as the accurate and reliable technology for biometric validation system. This review paper is all about Face recognition techniques in biometric locking system and Iris recognition technique of identification and the ways of making locking systems ways more efficient, full of ease, more secure, and far better than before so as to make locking or security stronger. It discusses about face recognition technique, its working and its application in different sector along with iris recognition, its working, its application.

A Mixed Method Review of Tobacco Cessation for the Cardiopulmonary Rehabilitation Clinician.

To systematically survey the literature, describe the current tobacco science, and perform a mixed method review of randomized control trials of tobacco research in the cardiopulmonary population. Mixed method review was conducted on major resource databases. Inclusion criteria were English language with a minimum follow-up of 6 months, published between January 1, 2007, and June 30, 2016; adult smokers ≥18 years of age with cardiovascular and/or pulmonary disease; initiation of subject recruitment from hospital or community; tobacco cessation (TC) as the main aim of the study; biometric validation of smoking status; first-line TC medications; and nonpharmacological treatments. The pooling of the 10 studies through forest plot analysis revealed the effect of tobacco continuous abstinence rates significant at 3, 6, and 12 months (total OR = 3.73; 95% CI, 2.58-5.38). Also, tobacco point prevalence rates of TC treatments demonstrated overall effects that were significant at the different end points (total OR = 2.63; 95% CI, 1.90-3.64). In both cases, the higher ORs were found in the 3 months end point. Most successful interventions consisted of a combination of pharmacological and nonpharmacological therapy (predominantly counseling). The evidence continues to support the recommended first-line TC pharmacotherapy and nonpharmacological practices published in the 2008 national guidelines. Implications for cardiopulmonary rehabilitation clinicians are discussed.

Biometric validation of a virtual reality-based psychomotor test for motor skill training

ABSTRACTPsychomotor tests have been applied in clinical therapy and laboratory research as tools for evaluating motor and cognitive skills. Some studies have developed computerized versions of such tests using virtual reality (VR) systems with haptic interface controls. These systems allow for increased flexibility in test delivery and accuracy in performance assessment. In this study, a VR-based computer simulation of the block design (BD) test (a standardized psychomotor task as part of an adult IQ test) was developed and compared with the physical version of the test. Performance was evaluated based on four types of muscle activation collected using electromyography (EMG), time spent in completing the task, and subjective ratings of workload. Results verified the VR-based task as physically comparable to the conventional BD test. The validated computerized psychomotor task may be applied for both experimental and clinical use in future studies.

Biometric Validation by Storing different Patterns using Mouse Gesture Signatures

Biometric Validation by Storing different Patterns using Mouse Gesture Signatures

Multimodal Biometric Cryptosystem for Face and Ear Recognition Based on Fuzzy Vault

Multimodal biometrics technology that uses more than two sorts of biometrics data has been universally applied for person certification and proof. Researchers have advised that the ear may have benefits over the face for biometric recognition. In this study, a technique for face and ear recognition has suggested. The face image and ear images are prearranged as input. From the pre-processed input images, the shape and texture characteristics are removed. The shape of ear and face is attained by suggesting modified region growing algorithm and texture characteristic by Local Gabor XOR Pattern (LGXP) method. To produce the fuzzy vault, the multi-modal biometric template and the input key are employed. For working out, the multi-modal biometric template from face and ear will be erected and it is united with the stored fuzzy vault to produce the final key. Experimental results of suggested method explain promising development in multimodal biometric validation.

Abstract 5585: Development and validation of the specificity of a novel azulene-based COX2 probe for cancer imaging.

Abstract The overall objectives of this research are to (i) develop azulene-based PET probes and (ii) image COX2 as a potential biomarker of breast cancer. Several lines of research have demonstrated that COX2 is overexpressed in breast cancer and that its presence correlates with poor prognoses. While other studies have reported that COX2 inhibition can be modulated and used beneficially as a chemopreventive strategy in cancer, no viable mechanism for achieving that approach has yet been developed. This shortfall could be circumvented through in vivo imaging of COX2 activity, particularly using sensitive imaging techniques such as PET. Toward that goal, our laboratory focuses on the development of novel 18F-labled COX2 probes. We began the synthesis of the probes by transforming tropolone into a lactone, which was subjected to an [8+2] cycloaddition reaction to yield 2-methylazulene as the core ring of the probe. After exploring numerous synthetic routes, the final target molecule and precursor PET compounds were prepared successfully using convergent synthesis. Conventional 18F labeling methods caused precursor decomposition, which prompted us to hypothesize that the acidic protons of the methylene moiety between the azulene and thiazole rings were readily abstracted by a strong base such as potassium carbonate. Ultimately, this caused the precursors to disintegrate. This observation was supported after successfully using an 18F labeling strategy that employed a much milder phosphate buffer. The 18F-labeled COX2 probe was tested in a breast cancer preclinical mouse model. The data obtained via successive whole-body PET/CT scans indicated probe accumulation and retention in the tumor. Overall, the probe was stable in vivo and no defluorination was observed. A biodistribution study, using the PET probe and Western blot analysis corroborate with the imaging data. Taken together, these data demonstrate the potential for the use of this novel PET probe for imaging COX2 as a biomarker for the early detection and prediction of the progression of colorectal cancer. Currently, several probe optimization processes are presently being carried out in our laboratory, including the toxicity study and in vivo metabolite analysis. Detailed results of the biometric validation of the probe will be reported in a timely manner. In conclusion, this novel COX2 PET probe was shown to be a promising agent for cancer imaging and deserves further investigation. Citation Format: Wellington Pham, Donald D. Nolting, Dileep J.S. Kumar, John J. Mann, Mohammed N. Tantawy, Todd E. Peterson, Larry Marnett, John C. Gore. Development and validation of the specificity of a novel azulene-based COX2 probe for cancer imaging. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5585. doi:10.1158/1538-7445.AM2013-5585

Biometrics underpin elections and benefits in the Philippines

The Philippines has introduced mandatory voter registration. The biometric validation process will begin on 1 July.

Abstract 3176: Preclinical validation of the sensitivity and specificity of a fluorescent nanobeacon for its application in colonoscopy-based imaging of colorectal cancer

Abstract Colorectal cancer is the third most commonly diagnosed type of cancer and the second leading cause of cancer-related death in the U.S. Currently, the key element in the control, prevention, and treatment of this disease is recognition of its early symptoms. In this work, we describe the development and validation of a specific fluorescent nanobeacon that targets the Thomsen-Friedenreich (Tf) antigen, potential biomarkers of this disease, via the terminal carbohydrate Gal-β(1-3)GalNAc. The imaging agent is comprised of a submicron-sized polystyrene nanosphere encapsulated with a hydrophobic fluorescent dye. The surface of the nanosphere was modified with the peanut agglutinin (PNA) and poly(N-vinylacetamide (PNVA) moieties. The former binds to Gal-β(1-3)GalNAc with high affinity, while the latter enhances the specificity of PNA for the carbohydrates. This nanobeacon detects colorectal tumors by recognizing the tumor-specific antigen through the surface-immobilized PNA, and identification is made via the fluorescence signal obtained from the coumarin-6 encapsulated in the nanosphere's core. Biorecognition by the probe has been demonstrated in cell lines and orthotopic mouse models. Here, we assessed the sensitivity and specificity of the probe in a pre-clinical MUC1 transgenic mouse model. Of note, the probe reported that the Tf antigen expression level is high in the colon and intestine of that model compared to wild- type mice. Data obtained by fluorescence imaging using this probe correlates well with those obtained via immunohistochemistry. Most importantly, the probe was not absorbed systemically upon topical application in the host. As a result, no registered toxicity is associated with the probe. Using the Tf antigen imaging approach, we are evaluating the binding specificity of the nanobeacon in panel of human colorectal cancer tissues. Thus far, the study has indicated quantitatively a 5-fold enhancement of the Tf expression in colon cancer tissue versus the control tissue. In sum, these data demonstrate the potential for the use of this novel nanobeacon in imaging the Tf antigen as a biomarker for the early detection and prediction of the progression of colorectal cancer. Currently, several probe optimization processes are presently being carried out in our laboratory, including the incorporation of near-infrared fluorescent dyes to enhance the signal-to-noise ratio. Detailed results of the biometric validation of the nanobeacon will be reported in a timely manner. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3176. doi:10.1158/1538-7445.AM2011-3176