BITS Faculty Publications
Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867
Browse
38 results
Search Results
Item 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%.Item Next Generation Systems and Networks(Springer, 2023) Bansal, Hari Om; Ajmera, Pawan K.; Joshi, SandeepThe book is a collection of high-quality research papers presented at International Conference on Next Generation Systems and Networks (BITS EEE CON 2022), held at Birla Institute of Technology & Science, Pilani, Rajasthan, India, during November 4–5, 2022. This book provides reliable and efficient design solutions for the next-generation networks and systems. The book covers research areas in energy, power and control; communication and signal processing; and electronics and nanotechnology.Item Analytical study on users’ awareness and acceptability towards adoption of multimodal biometrics (MMB) mechanism in online transactions: a two-stage SEM-ANN approach(Springer, 2022-09) Ajmera, Pawan K.The study analyses user awareness of multimodal biometrics and its acceptability for online transactions in the current dynamic world. The study was performed on the five underlying perspectives: User Acceptability, Cognizant Factors towards Biometrics, Technological factors, Perceptional Factors (Fingerprints, Iris, Face Recognition and Voice) and Data Privacy Factors. A questionnaire was prepared and circulated to the 530 biometrics users; on that basis, the corresponding answer was obtained for analysis. SEM is first employed to gauge the research model and test the prominent hypothesized predictors, which are then used as inputs in the neural network to evaluate the relative significance of each predictor variable. By considering the standardized significance of the feed-for-back-propagation of ANN algorithms, the study found a significant effect of DPF_3 (93%), DPF_2 (50%) and DPF_4 (34%) on the adoption of MMB. In the Perceptional construct, PRF_2 (49%) and PRF_3 (33%) was relatively the most important predictor, whereas, in User Acceptability, UAC_2 (37%), UAC_3 & UAC_5 (41%) was vital to be considered. Only one item, TCF_2 (35%), from Technological Factors, followed by Cognizant factors, i.e., CFG_1 (33%), confirmed the best fit model to adopt MMB. The research is a novel effort when compared to past studies as it considered cognizant and perceptual factors in the proposed model, thereby expanding the analytical outlook of MMB literature. Thus, the study also explored several new and valuable practical implications for adopting multimodal instruments of biometrics along with certain limitations.Item 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.Item Robust Palm-print Recognition Using Multi-resolution Texture Patterns with Artificial Neural Network(Springer, 2024-01) Ajmera, Pawan K.Palm-print recognition system is extensively deployed in a variety of applications, ranging from forensic to mobile phones. This paper proposes a new feature extraction technique for robust palm-print based recognition. The method combines the angle information of an edge operator and multi-scale uniform patterns, which extracts texture patterns at different angular space and spatial resolution. Thus, making the extracted uniform patterns less sensitive to the pixel level values. Further, an optimal artificial neural network structure is developed for classification, which helps in maintaining the higher classification accuracy by significantly reducing the computational complexity. The proposed method is tested on standard PolyU, IIT-Delhi and CASIA palm-print databases. The method yields an equal error rate of 0.2% and classification accuracy of 98.52% on PolyU databaseItem AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection(IEEE, 2023) Ajmera, Pawan K.This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. We also develop a custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including common rust, grey leaf spot and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops using advanced machine learning techniques and deep learning architectures.Item APiCroDD: Automated Pipeline for Crop Disease Detection(Springer, 2024) Ajmera, Pawan K.This research paper proposes APiCroDD: automated pipeline for crop disease detection, an automated framework for early detection of plant diseases using multispectral imagery from drones. Current frameworks for disease detection are labor and time-consuming. They do not leverage the richness of multispectral imagery for feature extraction and perform vanilla manipulation of agriculture indices. Our framework comprises two stages: data acquisition and disease identification. We find that the use of multispectral imagery in the proposed framework provides several advantages over traditional RGB imagery, including better spectral resolution and increased sensitivity to subtle changes in plant health. The multispectral data enables the identification of specific spectral bands associated with diseased regions of the plant, improving the accuracy of disease detection. The proposed framework utilizes a combination of CNNs and segmentation techniques to identify the plant and its disease. Experimental results demonstrate that the proposed framework using EfficientNet is highly effective in identifying a range of plant diseases achieving state-of-the-art performance on manually collected dataset and validated on the PlantVillage dataset.Item Multiresolution Features Based Polynomial Kernel Discriminant Analysis for Speaker Recognition(IEEE, 2009) Ajmera, Pawan K.This paper describes polynomial kernel subspace approach to speaker recognition systems. Auditory motivated wavelet packet transform is used to derive the desirable speaker features. The nonlinear mapping between the input space and the feature space is implicitly performed using the kernel trick. This nonlinear mapping increases the discrimination capability of a pattern classifier. The use of Mel-scale based and Bark-scale based wavelet packet trees for feature extraction process adds human auditory perception behavior to enhance the classification performance. Experimental results show that the proposed kernel based technique is computationally efficient and performs well with less training data.Item Robust feature extraction from spectrum estimated using bispectrum for Isolated Word Recognition(IEEE, 2011) Ajmera, Pawan K.Extraction of robust features from noisy speech signals is one of the challenging problems in Automatic Speech Recognition (ASR). For Gaussian process, its bispectrum and all higher order spectra are identically zero, which means that bispectrum removes the additive white Gaussian noise while preserving the magnitude and phase information of original signal. Using this bispectrum property, spectrum of original signal can be recovered from its noisy version. Robust Mel Frequency Cepstral Coefficients (MFCC) are extracted from the estimated spectral magnitude (denoted as Bispectral-MFCC (BMFCC)). The effectiveness of BMFCC has been tested on TI-46 isolated word database in noisy (additive white Gaussian) environment. The experimental results show the superiority of the proposed technique over conventional methods for Isolated Word Recognition (IWR).Item Real time human face location and recognition system using single training image per person(IEEE, 2011) Ajmera, Pawan K.This paper presents an automatic real time face location and recognition system. The proposed approach detects the face using the combination of hue, saturation and intensity (H SI) and luminance, red chrominance and blue chrominance (Y CrCb) color Space models. The left most, right most and top most pixels of face are detected using threshold values of parameters. One of the eyes is located using the blue chrominance. The second eye, center of the eyes, and the bottom most part of face is detected using geometrical similarity. The face is cropped using these defined boundaries to extract facial region only. The facial features of cropped image are extracted using the combination of Radon and wavelet transform. The technique computes Radon projections in different orientations and captures the directional features of face images. Further the wavelet transform applied on Radon space provides multiresolution features of the facial images. For classification, the nearest neighbor classifier has been used. The performance and robustness of the proposed system is tested on a face database of 785 images of 157 subjects acquired in conditions similar to those encountered in real world applications. The system achieves a recognition rate of 97.8 % and an equal error rate (EER) of about 2.4% for 157 subjects.