Analyzing eyebrow region for morphed image detection

Facial images in passports are designated as primary identifiers for the verification of travelers according to the International Civil Aviation Organization (ICAO). Hence, it is important to ascertain the sanctity of the facial images stored in the electronic Machine-Readable Travel Document (eMRTD). With the introduction of automated border control (ABC) systems that rely on face recognition for the verification of travelers, it is even more crucial to have a system to ensure that the image stored in the eMRTD is free from any alteration that can hinder or abuse the normal working of a facial recognition system. One such attack against these systems is the face-morphing attack. Even though many techniques exist to detect morphed images, morphing algorithms are also improving to evade these detections. In this work, we analyze the eyebrow region for morphed image detection. The proposed method is based on analyzing the frequency content of the eyebrow region. The method was evaluated on two datasets that each consisted of morphed images created using two algorithms. The findings suggest that the proposed method can serve as a valuable tool in morphed image detection, and can be used in various applications where image authenticity is critical

CLASSIFICATION OF LARGE SCALE FISH DATASET BY DEEP NEURAL NETWORKS

The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture, which is famous for faster training and lower computational cost. In CCT, data augmentation techniques are employed to enhance the variability of the training data, reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of 100% over the Large-scale fish dataset, with the potential for real-time deployment in aquatic monitoring systems. Furthermore, this work proposes avenues for future research in the domain of fish classification

Fusion features ensembling models using Siamese convolutional neural network for kinship verification

Family is one of the most important entities in the community. Mining the genetic information through facial images is increasingly being utilized in wide range of real-world applications to facilitate family members tracing and kinship analysis to become remarkably easy, inexpensive, and fast as compared to the procedure of profiling Deoxyribonucleic acid (DNA). However, the opportunities of building reliable models for kinship recognition are still suffering from the insufficient determination of the familial features, unstable reference cues of kinship, and the genetic influence factors of family features. This research proposes enhanced methods for extracting and selecting the effective familial features that could provide evidences of kinship leading to improve the kinship verification accuracy through visual facial images. First, the Convolutional Neural Network based on Optimized Local Raw Pixels Similarity Representation (OLRPSR) method is developed to improve the accuracy performance by generating a new matrix representation in order to remove irrelevant information. Second, the Siamese Convolutional Neural Network and Fusion of the Best Overlapping Blocks (SCNN-FBOB) is proposed to track and identify the most informative kinship clues features in order to achieve higher accuracy. Third, the Siamese Convolutional Neural Network and Ensembling Models Based on Selecting Best Combination (SCNN-EMSBC) is introduced to overcome the weak performance of the individual image and classifier. To evaluate the performance of the proposed methods, series of experiments are conducted using two popular benchmarking kinship databases; the KinFaceW-I and KinFaceW-II which then are benchmarked against the state-of-art algorithms found in the literature. It is indicated that SCNN-EMSBC method achieves promising results with the average accuracy of 92.42% and 94.80% on KinFaceW-I and KinFaceW-II, respectively. These results significantly improve the kinship verification performance and has outperformed the state-of-art algorithms for visual image-based kinship verification

Application of Wavelet Analysis and Random Field in Integrity Management of Pipelines Containing Dents and Corrosions

Metal loss corrosions and dents are two major threats to the integrity of oil and natural gas pipelines. In the pipeline industry, the Fitness-For-Service (FFS) assessment is commonly employed for pipelines containing these defects. However, FFS assessment usually assumes that a defect has a simple shape, and such a simplification may significantly affect the accuracy of the assessment. Therefore, retaining the actual shapes of defects and incorporating them into the FFS assessment can improve assessment accuracy. The main objective of the present thesis is to extract key information about the sizes, directions, and shapes of corrosions and dents from the measurement of in-service and excavated pipelines, and then improve the accuracy of FFS assessment based on the extracted information. The first study develops a wavelet transform-based denoising method for the measured inner surface of in-service dented pipelines obtained from caliper tools. Since the inner surface is differently sampled along the longitudinal and circumferential directions, the commonly used denoising methods cannot sufficiently remove measurement errors from the signal. The proposed method is based on overcomplete expansion, and the overcomplete dictionary is constructed from the hyperbolic wavelet transform and stationary transform. The strain estimated from the signal denoised by the proposed method is closer to the actual strain than the other denoising method. An overcomplete dictionary that can effectively denoise the dent signal is then constructed based on the statistics of the measurement of in-service dented pipelines. The second study explores the vital directional features and length scales of natural corrosion clusters that govern the burst capacity of corroded pipelines. The corrosion depths in a cluster are measured by high-resolution laser scans, and two-dimensional (2D) discrete wavelet transform (DWT) with a suitable wavelet function is employed to decompose the corrosion cluster. A methodology is proposed to determine level- and sub-band-dependent thresholds such that those wavelet coefficients below the thresholds have a negligible impact on the burst capacity predicted by the widely used RSTRENG model and can be ignored for the reconstruction of the cluster. The preserved wavelet coefficients show that longitudinally orientated features with 4 – 32 mm in length have a greater influence on the remaining burst capacity than other features. This facilitates FFS assessment of corroded pipelines. The third study aims to simulate the corrosion fields whose morphology and marginal distribution are close to the actual corrosion fields from limited information summarized from the ILI data. The corrosion field containing multiple corrosion anomalies is modelled as a nonhomogeneous non-Gaussian random field, where the spatial correlation and marginal distribution of anomalies are estimated from their sizes. The proposed methodology provides realizations of corrosion fields with the RSTRENG-predicted burst capacity closer to the actual burst capacity than the commonly used methodology that idealizes anomalies as cuboids. The fourth study presents a framework to analyze and simulate nonhomogeneous non-Gaussian corrosion fields on the external surface of buried in-service pipelines by using continuous and discrete wavelet transforms. Continuous wavelet transform (CWT), dual-tree complex discrete wavelet transform (DT-CDWT), and dual-tree complex discrete wavelet with hyperbolic wavelet transform scheme (DT-CHWT) are incorporated into the iterative power and amplitude correction (IPAC) algorithm to extract the features of the natural corrosion field measured by a high-resolution laser scan and generate synthetic corrosion fields. The results indicate that the proposed framework can generate synthetic corrosion fields that effectively capture probabilistic characteristics of the measured corrosion field in terms of the scalogram, textural features, and burst capacity of the pipe segment containing the corrosion field

Mixture-Based Clustering and Hidden Markov Models for Energy Management and Human Activity Recognition: Novel Approaches and Explainable Applications

In recent times, the rapid growth of data in various fields of life has created an immense need for powerful tools to extract useful information from data. This has motivated researchers to explore and devise new ideas and methods in the field of machine learning. Mixture models have gained substantial attention due to their ability to handle high-dimensional data efficiently and effectively. However, when adopting mixture models in such spaces, four crucial issues must be addressed, including the selection of probability density functions, estimation of mixture parameters, automatic determination of the number of components, identification of features that best discriminate the different components, and taking into account the temporal information. The primary objective of this thesis is to propose a unified model that addresses these interrelated problems. Moreover, this thesis proposes a novel approach that incorporates explainability. This thesis presents innovative mixture-based modelling approaches tailored for diverse applications, such as household energy consumption characterization, energy demand management, fault detection and diagnosis and human activity recognition. The primary contributions of this thesis encompass the following aspects: Initially, we propose an unsupervised feature selection approach embedded within a finite bounded asymmetric generalized Gaussian mixture model. This model is adept at handling synthetic and real-life smart meter data, utilizing three distinct feature extraction methods. By employing the expectation-maximization algorithm in conjunction with the minimum message length criterion, we are able to concurrently estimate the model parameters, perform model selection, and execute feature selection. This unified optimization process facilitates the identification of household electricity consumption profiles along with the optimal subset of attributes defining each profile. Furthermore, we investigate the impact of household characteristics on electricity usage patterns to pinpoint households that are ideal candidates for demand reduction initiatives. Subsequently, we introduce a semi-supervised learning approach for the mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. The integration of the uniform distribution within the inner mixture bolsters the model's resilience to outliers. In the unsupervised learning approach, the minimum message length criterion is utilized to ascertain the optimal number of mixture components. The proposed models are validated through a range of applications, including chiller fault detection and diagnosis, occupancy estimation, and energy consumption characterization. Additionally, we incorporate explainability into our models and establish a moderate trade-off between prediction accuracy and interpretability. Finally, we devise four novel models for human activity recognition (HAR): bounded asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(BAGGM-FSHMM), bounded asymmetric generalized Gaussian mixture-based hidden Markov model~(BAGGM-HMM), asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(AGGM-FSHMM), and asymmetric generalized Gaussian mixture-based hidden Markov model~(AGGM-HMM). We develop an innovative method for simultaneous estimation of feature saliencies and model parameters in BAGGM-FSHMM and AGGM-FSHMM while integrating the bounded support asymmetric generalized Gaussian distribution~(BAGGD), the asymmetric generalized Gaussian distribution~(AGGD) in the BAGGM-HMM and AGGM-HMM respectively. The aforementioned proposed models are validated using video-based and sensor-based HAR applications, showcasing their superiority over several mixture-based hidden Markov models~(HMMs) across various performance metrics. We demonstrate that the independent incorporation of feature selection and bounded support distribution in a HAR system yields benefits; Simultaneously, combining both concepts results in the most effective model among the proposed models

Digital Techniques for Documenting and Preserving Cultural Heritage

In this unique collection the authors present a wide range of interdisciplinary methods to study, document, and conserve material cultural heritage. The methods used serve as exemplars of best practice with a wide variety of cultural heritage objects having been recorded, examined, and visualised. The objects range in date, scale, materials, and state of preservation and so pose different research questions and challenges for digitization, conservation, and ontological representation of knowledge. Heritage science and specialist digital technologies are presented in a way approachable to non-scientists, while a separate technical section provides details of methods and techniques, alongside examples of notable applications of spatial and spectral documentation of material cultural heritage, with selected literature and identification of future research. This book is an outcome of interdisciplinary research and debates conducted by the participants of the COST Action TD1201, Colour and Space in Cultural Heritage, 2012–16 and is an Open Access publication available under a CC BY-NC-ND licence.https://scholarworks.wmich.edu/mip_arc_cdh/1000/thumbnail.jp