BITS Faculty Publications

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    Robust Multi-Spectral Palm-Print Recognition
    (Springer, 2023-07) Ajmera, Pawan K.
    In recent years, biometric has emerged as the most trustworthy and reliable technique for verifying or identifying humans. The distinctive and consistent qualities of palm prints have led to a rise in their use as a biometric attribute for user access and authentication. A palm-print template is proposed that stores the relative geometric information of the minutiae points. It is impossible to determine the orientation of the palm-print from the template since it lacks information on the orientation of the minutiae and their position. For template matching, an internal angle based on Delaunay triangulation is used, which then produces matching scores for various spectral bands. The multispectral data is finally integrated using a score-level fusion approach that minimizes the overlapping effects. The technique was evaluated on the widely used PolyU multispectral palm-print database, which provides more accurate results than a mono-spectral band. The total Correct Recognition Rate (CRR) is 96.28%, while the Equal Error Rate (EER) is 0.16%.
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    Multi-view Feature Learning Based on Texture Description for Palm-Print Recognition
    (Springer, 2023-09) Ajmera, Pawan K.
    Biometric is the science of validating an individual’s identity while using behavioral and physiological characteristics. In unconstrained scenario, contactless palm-print recognition leads to better recognition accuracy of individuals. Most of the existing texture descriptors are fail to learn stable and discriminative features from palm-print images. The paper presents a multi-view feature learning method based on texture description for palm-print recognition. The multi-view features are simultaneously extracted by two complementary operators. We also learn how to use feature mapping to convert multi-view data into hash codes. Experiments are carried out on palm-print databases captured using a variety of devices and acquisition methods. We demonstrate that the proposed method has superior performance compared to the current methods.
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    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.
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    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.
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    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.
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    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.
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    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.
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    Palm-print recognition based on scale invariant features
    (IEEE, 2019) Ajmera, Pawan K.
    Over the past few years, palm-print recognition has proved to be one of the extensively used technology for human identification/verification in many aspects. This paper presents the implementation of five feature extraction algorithms such as Mean, AAD (Average Absolute Deviation), GMF (Gaussian Membership Function) along with SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Feature) for effective recognition. SVM (Support Vector Machine) and KNN (K-Nearest Neighbor) are the machine learning algorithms used for the classification of data. Experimentations are carried out on the PolyU and IIT-Delhi palm-print databases. The scale invariant features of SURF provides the best performance with Correct Recognition Rate (CRR) of 99.56% and 97.95% for IIT-Delhi and PolyU palm-print database respectively.
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    Palm-print identification based on deep residual networks
    (IEEE, 2021) Ajmera, Pawan K.
    Biometric recognition has been an inseparable part of security and authorization. In the last decade, palm-print has been widely used in security access and person authentication. However, for efficient identity management and access regulation neural network based classification algorithms are required as they provide an efficient means of adaptive feature extraction using back-propagation, leading to better classification results. This paper presents the implementation of various neural networks for an efficient palm-print classification. The model is trained using the ResNet-18, ResNet-50 and ResNet-101 architectures using the PolyU and IIT-Delhi palm-print databases. The evaluation of the performance parameters indicate that the ResNet with SURF features provides the best results in lesser number of epochs. The results obtained are significantly better than the traditional methods.
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    Upgrading security and protection in ear biometrics
    (IET, 2019-02) Ajmera, Pawan K.
    Biometrics is being widely accepted for user authentication across the globe. Integration of biometrics in the daily life provokes the need to design secure authentication systems. This study proposes the use of outer ear images as a biometric modality. The comparable complexity between the human outer ear and face in terms of its uniqueness and permanence has increased interest in the use of ear as a biometric. However, similar to face recognition, it poses challenges of variation in illumination, contrast, rotation, scale and pose. Owing to the extensive work in the field of computer vision using convolutional neural networks (CNNs), its feasibility in the field of ear biometrics has been presented in this work. The proposed technique uses a CNN as a feature extractor and a support vector machine (SVM) for the classification task. The joint CNN-SVM framework is used for mapping ear images to random base-n codes. The codes are further hashed using the secure hash algorithm SHA-3 to generate secure ear templates. The feasibility of the proposed technique has been evaluated on annotated web ears dataset. This work demonstrates 12.52% average equal error rate without any image pre-processing, which shows that the proposed approach is promising in the field of secure ear biometrics.