979 research outputs found

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    A False Sense of Privacy: Towards a Reliable Evaluation Methodology for the Anonymization of Biometric Data

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    Biometric data contains distinctive human traits such as facial features or gait patterns. The use of biometric data permits an individuation so exact that the data is utilized effectively in identification and authentication systems. But for this same reason, privacy protections become indispensably necessary. Privacy protection is extensively afforded by the technique of anonymization. Anonymization techniques protect sensitive personal data from biometrics by obfuscating or removing information that allows linking records to the generating individuals, to achieve high levels of anonymity. However, our understanding and possibility to develop effective anonymization relies, in equal parts, on the effectiveness of the methods employed to evaluate anonymization performance. In this paper, we assess the state-of-the-art methods used to evaluate the performance of anonymization techniques for facial images and for gait patterns. We demonstrate that the state-of-the-art evaluation methods have serious and frequent shortcomings. In particular, we find that the underlying assumptions of the state-of-the-art are quite unwarranted. State-of-the-art methods generally assume a difficult recognition scenario and thus a weak adversary. However, that assumption causes state-of-the-art evaluations to grossly overestimate the performance of the anonymization. Therefore, we propose a strong adversary which is aware of the anonymization in place. This adversary model implements an appropriate measure of anonymization performance. We improve the selection process for the evaluation dataset, and we reduce the numbers of identities contained in the dataset while ensuring that these identities remain easily distinguishable from one another. Our novel evaluation methodology surpasses the state-of-the-art because we measure worst-case performance and so deliver a highly reliable evaluation of biometric anonymization techniques

    Face Recognition Based Attendance System Using Histogram of Oriented Gradients and Linear Support Vector Machine

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    In the 21st century, modern technology is playing an important role in providing innovative on traditional challenges across various domains or sectors. One such challenging task is of daily attendance marking and tracking. Manual attendance requires efforts and it is time-consuming. Sometimes attendance cannot be mark due to human errors. Relying on voice, iris, or fingerprint recognition, increases the complexity and the hardware infrastructure of the system and also increases the cost. To effectively address such issues, we have developed a “Camera based Attendance System”. This system encompasses several crucial stages, including data entry, dataset of multiple people. It is an image-based face recognition system for marking attendance on the SQL database. It excels in detecting and recognizing multiple individuals faces from image and comparing it with the dataset for accurately marking the attendance. This makes the attendance marking process fully automatic. Remarkably, our proposed system attains an impressive recognition and provides the accuracy of approximately 95%. With this solution, daily attendance marking and recording becomes effortless and the stored attendance record can be also used in future if require, eliminating the risk of attendance not getting marked due to human error.&nbsp

    Optimising multimodal fusion for biometric identification systems

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    Biometric systems are automatic means for imitating the human brain’s ability of identifying and verifying other humans by their behavioural and physiological characteristics. A system, which uses more than one biometric modality at the same time, is known as a multimodal system. Multimodal biometric systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. This thesis addresses some issues related to the implementation of multimodal biometric identity verification systems. The thesis assesses the feasibility of using commercial offthe-shelf products to construct deployable multimodal biometric system. It also identifies multimodal biometric fusion as a challenging optimisation problem when one considers the presence of several configurations and settings, in particular the verification thresholds adopted by each biometric device and the decision fusion algorithm implemented for a particular configuration. The thesis proposes a novel approach for the optimisation of multimodal biometric systems based on the use of genetic algorithms for solving some of the problems associated with the different settings. The proposed optimisation method also addresses some of the problems associated with score normalization. In addition, the thesis presents an analysis of the performance of different fusion rules when characterising the system users as sheep, goats, lambs and wolves. The results presented indicate that the proposed optimisation method can be used to solve the problems associated with threshold settings. This clearly demonstrates a valuable potential strategy that can be used to set a priori thresholds of the different biometric devices before using them. The proposed optimisation architecture addressed the problem of score normalisation, which makes it an effective “plug-and-play” design philosophy to system implementation. The results also indicate that the optimisation approach can be used for effectively determining the weight settings, which is used in many applications for varying the relative importance of the different performance parameters

    Improved Human Face Recognition by Introducing a New Cnn Arrangement and Hierarchical Method

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    Human face recognition has become one of the most attractive topics in the fields ‎of biometrics due to its wide applications. The face is a part of the body that carries ‎the most information regarding identification in human interactions. Features such ‎as the composition of facial components, skin tone, face\u27s central axis, distances ‎between eyes, and many more, alongside the other biometrics, are used ‎unconsciously by the brain to distinguish a person. Indeed, analyzing the facial ‎features could be the first method humans use to identify a person in their lives. ‎As one of the main biometric measures, human face recognition has been utilized in ‎various commercial applications over the past two decades. From banking to smart ‎advertisement and from border security to mobile applications. These are a few ‎examples that show us how far these methods have come. We can confidently say ‎that the techniques for face recognition have reached an acceptable level of ‎accuracy to be implemented in some real-life applications. However, there are other ‎applications that could benefit from improvement. Given the increasing demand ‎for the topic and the fact that nowadays, we have almost all the infrastructure that ‎we might need for our application, make face recognition an appealing topic. ‎ When we are evaluating the quality of a face recognition method, there are some ‎benchmarks that we should consider: accuracy, speed, and complexity are the main ‎parameters. Of course, we can measure other aspects of the algorithm, such as size, ‎precision, cost, etc. But eventually, every one of those parameters will contribute to ‎improving one or some of these three concepts of the method. Then again, although ‎we can see a significant level of accuracy in existing algorithms, there is still much ‎room for improvement in speed and complexity. In addition, the accuracy of the ‎mentioned methods highly depends on the properties of the face images. In other ‎words, uncontrolled situations and variables like head pose, occlusion, lighting, ‎image noise, etc., can affect the results dramatically. ‎ Human face recognition systems are used in either identification or verification. In ‎verification, the system\u27s main goal is to check if an input belongs to a pre-determined tag or a person\u27s ID. ‎Almost every face recognition system consists of four major steps. These steps are ‎pre-processing, face detection, feature extraction, and classification. Improvement ‎in each of these steps will lead to the overall enhancement of the system. In this ‎work, the main objective is to propose new, improved and enhanced methods in ‎each of those mentioned steps, evaluate the results by comparing them with other ‎existing techniques and investigate the outcome of the proposed system.

    A Novel Approach for Optimization of Convolution Neural Network with Particle Swarm Optimization and Genetic Algorithm for Face Recognition

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    Convolutional neural networks are contemporary deep learning models that are employed for many various applications. In general, the filter size, number of filters, number of convolutional layers, number of fully connected layers, activation function and learning rate are some of the hyperparameters that significantly determine how well a CNN performs.. Generally, these hyperparameters are selected manually and varied for each CNN model depending on the application and dataset. During optimization, CNN could get stuck in local minima. To overcome this, metaheuristic algorithms are used for optimization. In this work, the CNN structure is first constructed with randomly chosen hyperparameters and these parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. A CNN with optimized hyperparameters is used for face recognition. CNNs optimized with these algorithms use RMSprop optimizer instead of stochastic gradient descent. This RMSprop optimizer helps the CNN reach global minimum quickly. It has been observed that optimizing with GA and PSO improves the performance of CNNs. It also reduces the time it takes for the CNN to reach the global minimum

    Face recognition using skin texture

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    In today's society where information technology is depended upon throughout homes, educational establishments and workplaces the challenge of identity management is ever growing. Advancements in image processing and biometric feature based identification have provided a means for computer software to accurately identify individuals from increasingly vast databases of users. In the quest to improve the performance of such systems in varying environmental conditions skin texture is here proposed as a biometric feature. This thesis presents and discusses a hypothesis for the use of facial skin texture regions taken from 2-dimensional photographs to accurately identify individuals using three classifiers (neural network, support vector machine and linear discriminant). Gabor wavelet filters are primarily used for feature extraction and arc supported in later chapters by the grey-level cooccurrence probability matrix (GLCP) to strengthen the system by providing supplementary high-frequency features. Various fusion techniques for combining these features are presented and their perfonnance is compared including both score and feature fusion and various permutations of each. Based on preliminary results from the BioSecure Multimodal Database (BMDB) , the work presented indicates that isolated texture regions of the human face taken from under the eye may provide sufficient information to discriminately identify an individual with an equal error rate (EER) of under 1% when operating in greyscale. An analysis of the performance of the algorithm against image resolution investigates the systems performance when faced with lower resolution training images and discusses optimal resolutions for classifier training. The system also shows a good degree of robustness when the probe image resolution is reduced indicating that the algorithm provides some level of scale invariance. Scope for future work is laid out and a review of the evaluation is also presented

    Face Detection and Recognition Using Raspberry PI Computer

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    This paper presents a face detection and recognition system utilizing a Raspberry Pi computer that is built on a predefined framework. The theoretical section of this article shows several techniques that can be used for face detection, including Haar cascades, Histograms of Oriented Gradients, Support Vector Machine and Deep Learning Methods. The paper also provides examples of some commonly used face recognition techniques, including Fisherfaces, Eigenfaces, Histogram of Local Binary Patterns, SIFT and SURF descriptor-based methods and Deep Learning Methods. The practical aspect of this paper demonstrates use of a Raspberry Pi computer, along with supplementary tools and software, to detect and recognize faces using a pre-defined dataset

    Investigating Ensembles of Single-class Classifiers for Multi-class Classification

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    Traditional methods of multi-class classification in machine learning involve the use of a monolithic feature extractor and classifier head trained on data from all of the classes at once. These architectures (especially the classifier head) are dependent on the number and types of classes, and are therefore rigid against changes to the class set. For best performance, one must retrain networks with these architectures from scratch, incurring a large cost in training time. As well, these networks can be biased towards classes with a large imbalance in training data compared to other classes. Instead, ensembles of so-called ``single-class'' classifiers can be used for multi-class classification by training an individual network for each class.We show that these ensembles of single-class classifiers are more flexible to changes to the class set than traditional models, and can be quickly retrained to consider small changes to the class set, such as by adding, removing, splitting, or fusing classes. As well, we show that these ensembles are less biased towards classes with large imbalances in their training data than traditional models. We also introduce a new, more powerful single-class classification architecture. These models are trained and tested on a plant disease dataset with high variance in the number of classes and amount of data in each class, as well as on an Alzheimer's dataset with low amounts of data and a large imbalance in data between classes

    Realtime human face tracking and recognition system on uncontrolled environment

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    Recently, one of the most important biometrics is that automatically recognized human faces are based on dynamic facial images with different rotations and backgrounds. This paper presents a real-time system for human face tracking and recognition with various expressions of the face, poses, and rotations in an uncontrolled environment (dynamic background). Many steps are achieved in this paper to enhance, detect, and recognize the faces from the image frame taken by web-camera. The system has three steps: the first is to detect the face, Viola-Jones algorithm is used to achieve this purpose for frontal and profile face detection. In the second step, the color space algorithm is used to track the detected face from the previous step. The third step, principal component analysis (eigenfaces) algorithm is used to recognize faces. The result shows the effectiveness and robustness depending on the training and testing results. The real-time system result is compared with the results of the previous papers and gives a success, effectiveness, and robustness recognition rate of 91.12% with a low execution time. However, the execution time is not fixed due depending on the frame background and specification of the web camera and computer
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