Department of Computer Science and Information Systems

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1928

Browse

Has files: NoSubject: search.filters.subject.Biometrics

Search Results

Now showing 1 - 10 of 10
  • No Thumbnail Available
    Item
    Digital Signature using Biometrics
    (WCECS, 2015) Gupta, Shashank
    It is desirable to generate a digital signature using biometrics but not practicable because of its inaccurate measuring and complex methodologies, without using specific hardware devices that hold signature keys or biometric templates securely. Proposed model resolves the problem in biometric based digital signature by making it simple and secure. Proposed model uses the biometric template and generate the key which uses the AES which is much secure to make the signature useful.
  • No Thumbnail Available
    Item
    Deep Ear Biometrics for Gender Classification
    (Springer, 2023-07) Bera, Asish
    Human gender classification based on biometric features is a major concern for computer vision due to its vast variety of applications. The human ear is popular among researchers as a soft biometric trait, because it is less affected by age or changing circumstances and is non-intrusive. In this study, we have developed a deep convolutional neural network (CNN) model for automatic gender classification using the samples of ear images. The performance is evaluated using four cutting-edge pre-trained CNN models. In terms of trainable parameters, the proposed technique requires significantly less computational complexity. The proposed model has achieved 93% accuracy on the EarVN1.0 ear dataset.
  • No Thumbnail Available
    Item
    PalmHashNet: Palmprint Hashing Network for Indexing Large Databases to Boost Identification
    (IEEE, 2021) Bhatia, Ashutosh; Tiwari, Kamlesh
    Palmprint identification aims to establish the identity of a given query sample by comparing it with all the templates in the database and locating the most similar one. It becomes computationally expensive as the size of the database grows. It is because the number of comparisons becomes proportional to the number of templates stored in the database. The process needs to be accelerated to get a response in real-time, especially for large databases. This paper proposes a palmprint database indexing approach called PalmHashNet that generates highly discriminative embeddings to create a fixed-size candidate list for comparison to make identification a constant time operation. Acquired palmprint images are fed to the feature extraction network, which is pre-trained using softmax loss. A margin is added to the softmax loss to minimize the intra-class distance between samples belonging to the same class. It ensures that the features have high intra-class and low inter-class similarity. k-means and locality sensitive hashing (LSH) is investigated for index table creation. In this setting, cluster centers for k-means and hash values in the case of LSH serve as indices. The features are extracted for a given query palmprint and compared with the index values. The candidates lying in the most similar bin are retrieved for identification. The advantage of the proposed approach is that the query palmprint is compared with a small percentage of database instead of the whole. The proposed approach offers probabilistic guarantees for query identification in the selected bin. Experiments are conducted on four widely used palmprint databases viz . CASIA, IITD-Touchless, Tongji-Contactless and Hong Kong Polytechnic University Palmprint II (PolyU II). A penetration rate of 0.022%, 1.032%, 4.555%, and 0.39% at 100% hit rate is achieved on these databases, respectively. It makes the identification process approximately 4500, 96, 21, and 256 times faster on the respective databases.
  • No Thumbnail Available
    Item
    CP-Net: Multi-Scale Core Point Localization in Fingerprints Using Hourglass Network
    (IEEE, 2023) Bhatia, Ashutosh; Tiwari, Kamlesh
    Core point is a location that exhibits high curvature properties in a fingerprint. Detecting the accurate location of a core point is useful for efficient fingerprint matching, classification, and identification tasks. This paper proposes CP-Net, a novel core point detection network that comprises the Macro Localization Network (MLN) and the Micro-Regression Network (MRN). MLN is a specialized autoencoder network with an hourglass network at its bottleneck. It takes an input fingerprint image and outputs a region of interest that could be the most probable region containing the core point. The second component, MRN, regresses the RoI and locates the coordinates of the core point in the given fingerprint sample. Introducing an hourglass network in the MLN bottleneck ensures multi-scale spatial attention that captures local and global contexts and facilitates a higher localization accuracy for that area. Unlike existing multi-stage models, the components are stacked and trained in an end-to-end manner. Experiments have been performed on three widely used publicly available fingerprint datasets, namely, FVC2002 DB1A, FVC2004 DB1A, and FVC2006 DB2A. The proposed model achieved a true detection rate (TDR) of 98%, 100%, and 99.04% respectively, while considering 20 pixels distance from the ground truth as correct. Obtained experimental results on the considered datasets demonstrate that CP-Net outperforms the state-of-the-art core point detection techniques.
  • No Thumbnail Available
    Item
    Enhancing security through continuous biometric authentication using wearable sensors
    (Elsevier, 2024) Bhatia, Ashutosh; Tiwari, Kamlesh
    The paper presents a novel approach for biometric continuous driver authentication (CDA) for secure and safe transportation using wearable photoplethysmography (PPG) sensors and deep learning. Conventional one-time authentication (OTA) methods, while effective for initial identity verification, fail to continuously verify the driver’s identity during vehicle operation, potentially leading to safety, security, and accountability issues. To address this, we propose a system that employs Long Short-Term Memory (LSTM) models to predict subsequent PPG values from wrist-worn devices and continuously compare them with real-time sensor data for authentication. Our system calculates a confidence level representing the probability that the current user is the authorized driver, ensuring robust availability to genuine users while detecting impersonation attacks. The raw PPG data is directly fed into the LSTM model without pre-processing, ensuring lightweight processing. We validated our system with PPG data from 15 volunteers driving for 15 min in varied conditions. The system achieves an Equal Error Rate (EER) of 4.8%. Our results demonstrate that the system is a viable solution for CDA in dynamic environments, ensuring transparency, efficiency, accuracy, robust availability, and lightweight processing. Thus, our approach addresses the main challenges of classical driver authentication systems and effectively safeguards passengers and goods with robust driver authentication.
  • No Thumbnail Available
    Item
    Score level fusion of multimodal biometrics using triangular norms
    (Elsevier, 2011-10) Grover, Jyotsana
    A multimodal biometric system that alleviates the limitations of the unimodal biometric systems by fusing the information from the respective biometric sources is developed. A general approach is proposed for the fusion at score level by combining the scores from multiple biometrics using triangular norms (t-norms) due to Hamacher, Yager, Frank, Schweizer and Sklar, and Einstein product. This study aims at tapping the potential of t-norms for multimodal biometrics. The proposed approach renders very good performance as it is quite computationally fast and outperforms the score level fusion using the combination approach (min, mean, and sum) and classification approaches like SVM, logistic linear regression, MLP, etc. The experimental evaluation on three databases confirms the effectiveness of score level fusion using t-norms.
  • No Thumbnail Available
    Item
    Hand Biometric Verification with Hand Image-Based CAPTCHA
    (Springer, 2018-05) Bera, Asish
    An approach for hand biometric recognition with the hand image-based CAPTCHA verification is presented in this paper. A new method for CAPTCHA generation is implemented based on the genuine and fake hand images which are embedded in a complex textured color background image. The HandCaptcha is a useful application to differentiate between the human and automated scripts. The first level of security is achieved by the HandCaptcha against the malicious threats and attacks. After solving the HandCaptcha correctly, the identity of a person is authenticated based on the contact-less hand geometric verification approach in the second level. A set of 300 unique HandCaptcha is created randomly and solved by at least 100 persons with the accuracy of 98.34%. Next, the left-hand images of the legitimate users are normalized, and sixteen geometric features are computed from every normalized hand. Experiments are conducted on the 200 subjects of the Bosporus left-hand database. Classification accuracy of 99.5% has been achieved using the kNN classifier, and the equal error rate is 3.93%.
  • No Thumbnail Available
    Item
    Pose-Invariant Hand Geometry for Human Identification Using Feature Weighted k-NN Classifier
    (Springer, 2018-05) Bera, Asish
    Hand biometrics is globally deployed for automated human identification based on the discriminative geometric characteristics of hand. Advancements in hand biometric technologies are accomplished over several decades. The key objectives of this paper are two-fold. Firstly, it presents a comprehensive study on the state-of-the-art methods based on the hand images collected in an unconstraint environment. Secondly, a pose-invariant hand geometry system is excogitated. The experiments are conducted with the weighted geometric features computed from the fingers. The feature weighted k-nearest neighbor (fwk-NN) classifier is applied on the right- and left-hand images of the 500 subjects of the Bosphorus database for performance evaluation. The classification accuracy of 97% has been achieved for both of the hands using the fwk-NN classifier. Equal error rates (EER) of 5.94% and 6.08% are achieved for the right- and left-hand 500 subjects, respectively.
  • No Thumbnail Available
    Item
    Person recognition using alternative hand geometry
    (Inder Science, 2014-08) Bera, Asish
    In this paper, a new approach for user recognition is presented, which is based on the geometric features from either left or right hand images. The hand images are collected at unconstrained pose environment. Image normalisation is applied at the preprocessing stage. Features are extracted from the normalised images, which are mainly comprised of lengths and widths at different positions of the fingers. A simple classification algorithm has been implemented that is primarily dependent on the ratio of modified minimum distance and number of features, which are matched within a distance threshold. Experimental results of identification and verification are quite acceptable, producing 98.8% identification and 99.6% verification (at 0.55% FAR) of 253 standard subjects which are a blend of both left and right hand images.
  • No Thumbnail Available
    Item
    Finger contour profile based hand biometric recognition
    (Springer, 2016-10) Bera, Asish
    This paper presents a contactless hand biometric system at unrestricted hand pose environment. A new preprocessing technique is proposed for defining the finger contour profiles (FCP). It mainly consists of simple grayscale image transformation, subtraction, and logical XOR operation. This hand prototyping method logically decomposes global hand contour into the left and right contour profiles of each finger. A set of twenty pose-invariant geometric features is extracted from the FCP and normalized global hand shape. Experiments are conducted on two publicly available hand databases namely, the Bosphorus and IIT Delhi (IITD) databases to validate the system using the kNN, minimum distance, and random forest (RF) classifiers. Satisfactory identification accuracy of 97.82 % using the RF classifier has been achieved for the Bosphorus database with 320 subjects; and in verification, 3.28 % equal error rate (EER) is reported. The kNN classifier has been found to produce good identification success of 95.22 % for the IITD database of 230 subjects; and 4.76 % EER is obtained in verification. The average execution time of this approach is lesser than 2 s, that implies its suitability in real-world applications.