Background: With the rise of digitalization, there has been an increasing needfor secure and effective identification solutions, particularly in the realm of votingsystems. Facial biometric technology has emerged as a potential solution to combat fraud and improve the transparency and security of the voting process. Two well known facial identification algorithms, Local Binary Pattern Histograms (LBPH) and Eigenfaces, have been extensively used in computer vision for facial identification.However, their effectiveness in the context of a smart voting system is still a matter of debate. Objectives: The aim of this project is to compare the effectiveness of LBPH and Eigenfaces algorithms in the development of a smart voting system using the Haar cascade for face detection. The objective is to identify the more suitable approach between the two algorithms, considering factors such as lighting conditions and the facial expressions of the individuals being identified. The goal is to evaluate the algorithms using various metrics such as accuracy, precision, recall, and F1 score. Methods: The project involves the comparison of facial identification algorithms using the Haar cascade for face detection. Both the LBPH and Eigenfaces algorithms are implemented and evaluated in a complex environment that is similar to a polling station. The algorithms are trained and tested using a dataset of facial images with varying lighting conditions and facial expressions. The evaluation metrics, including accuracy, precision, recall, and F1 score, are used to compare the performance of thetwo algorithms. Results: The results of the project indicate that the LBPH algorithm performs better than Eigenfaces in terms of accuracy and performance. The algorithms havebeen tested with faces and objects in low-light conditions. Their accuracy and performance are also measured. Conclusions: The comparison of LBPH and Eigenfaces algorithms using the Haarcascade for face detection reveals that LBPH is a more suitable approach. The comparison of facial identification-based algorithms can significantly contribute to the voting process, thereby ensuring integrity of the voting process. The findings of this project can contribute to the development of a more reliable and secure voting system, and the evaluation metrics used in this project can be applied to future research in the field of facial identification purposes.

Face recognition is the problem of identifying individuals in images. This thesis evaluates two methods used to determine if pairs of face images belong to the same individual or not. The first method is a combination of principal component analysis and a neural network and the second method is based on state-of-the-art convolutional neural networks. They are trained and evaluated using two different data sets. The first set contains many images with large variations in, for example, illumination and facial expression. The second consists of fewer images with small variations. Principal component analysis allowed the use of smaller networks. The largest network has 1.7 million parameters compared to the 7 million used in the convolutional network. The use of smaller networks lowered the training time and evaluation time significantly. Principal component analysis proved to be well suited for the data set with small variations outperforming the convolutional network which need larger data sets to avoid overfitting. The reduction in data dimensionality, however, led to difficulties classifying the data set with large variations. The generous amount of images in this set allowed the convolutional method to reach higher accuracies than the principal component method.

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Yes / Principal component analysis (PCA) is an elegant mechanism that reduces the dimensionality of a dataset to bring out patterns of interest in it. The preprocessing of facial images for efficient face recognition is considered to be one of the epitomes among PCA applications. In this paper, we introduce a novel modification to the method of PCA whereby we propose to utilise the inherent averaging ability of the discrete Biharmonic operator as a preprocessing step. We refer to this mechanism as the BiPCA. Interestingly, by applying the Biharmonic operator to images, we can generate new images of reduced size while keeping the inherent features in them intact. The resulting images of lower dimensionality can significantly reduce the computational complexities while preserving the features of interest. Here, we have chosen the standard face recognition as an example to demonstrate the capacity of our proposed BiPCA method. Experiments were carried out on three publicly available datasets, namely the ORL, Face95 and Face96. The results we have obtained demonstrate that the BiPCA outperforms the traditional PCA. In fact, our experiments do suggest that, when it comes to face recognition, the BiPCA method has at least 25% improvement in the average percentage error rate.

Syftet med rapporten är att visa möjligheten att använda Raspberry Pi 2 i ett ansiktsigenkänningssystem. Studien redogör för prestandaskillnader mellan Eigenface och Fisherfacemetoden. Studieförfattarna har genomfört en experimentell studie enligt en kvantitativ metod där tester utgör empirin. Resultatet från testerna kommer presenteras genom diagram och påvisa möjligheten att använda Raspberry Pi 2 som hårdvara i ett ansiktsigenkänningssystem. Genom samma testutförande kommer skillnader mellan igenkänningsmetoderna att påvisas. Studien visar att Raspberry Pi 2 är en lämplig kandidat att använda för mindre ansiktsigenkänningssystem. Vidare framgår det att Fisherface-metoden är det lämpligaste valet att använda vid implementation av systemet. / The purpose with this report is to demonstrate the possibility to use Raspberry Pi 2 as hardware in a face recognition system. The study will show performance differences regarding the Eigenface- and Fisherface-method. To demonstrate the possibility the authors have done tests using an experimental study and quantitative method. To review the tests and to understand the result a qualitative literature review was taken. The tests will be presented as graphs to show the possibility to use Raspberry Pi 2 as hardware in a face recognition system. The same goes for the comparison of the chosen algorithms. The work indicates that Raspberry Pi 2 is a possible candidate to use for smaller face recognition systems. There is also an indication that the Fisherface method is the better choice for face recognition.

Identification of individuals has been solved with many different solutions around the world, either using biometric data or external means of verification such as id cards or RFID tags. The advantage of using biometric measurements is that they are directly tied to the individual and are usually unalterable. Acquiring dependable measurements is however challenging when the individuals are uncooperative. A dependable system should be able to deal with this and produce reliable identifications. The system proposed in this thesis can autonomously classify uncooperative specimens from depth data. The data is acquired from a depth camera mounted in an uncontrolled environment, where it was allowed to continuously record for two weeks. This requires stable data extraction and normalization algorithms to produce good representations of the specimens. Robust descriptors can therefore be extracted from each sample of a specimen and together with different classification algorithms, the system can be trained or validated. Even with as many as 138 different classes the system achieves high recognition rates. Inspired by the research field of face recognition, the best classification algorithm, the method of fisherfaces, was able to accurately recognize 99.6% of the validation samples. Followed by two variations of the method of eigenfaces, achieving recognition rates of 98.8% and 97.9%. These results affirm that the capabilities of the system are adequate for a commercial implementation.

All dairy cows in Europe wear unique identification tags in their ears. These eartags are standardized and contains the cows identification numbers, today only used for visual identification by the farmer. The cow also needs to be identified by an automatic identification system connected to milk machines and other robotics used at the farm. Currently this is solved with a non-standardized radio transmitter which can be placed on different places on the cow and different receivers needs to be used on different farms. Other drawbacks with the currently used identification system are that it is expensive and unreliable. This thesis explores the possibility to replace this non standardized radio frequency based identification system with a standardized computer vision based system. The method proposed in this thesis uses a color threshold approach for detection, a flood fill approach followed by Hough transform and a projection method for segmentation and evaluates template matching, k-nearest neighbour and support vector machines as optical character recognition methods. The result from the thesis shows that the quality of the data used as input to the system is vital. By using good data, k-nearest neighbour, which showed the best results of the three OCR approaches, handles 98 % of the digits.

In the last decades there has been such a fundamental development in the technologies including technologies focusing on face detection and identification supported by computer vision. Algorithm optimization has reached the point, when face detection is possible on mobile devices. At the outset, this work analy-ses common used algorithms for face detection and identification, for instance Haar features, LBP, EigenFaces and FisherFaces. Moreover, this work focuses on more up-to-date approaches of this topic, such as convolutional neural networks, or FaceNet from Google. The goal of this work is a design and its subsequent im-plementation of an automated, monitoring system designated for a lab, which is based on aforementioned algorithms. Within the design of the monitoring system, algorithms are compared with each other and their success rate and possible ap-plication in the final solution is evaluated.

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine which method has the highest accuracy of prediction rate. The three methods are reviewed and then compared via experiments. OpenCV, CMake, and Visual Studios were used as tools to conduct experiments. Analysis were conducted to identify which method has the highest accuracy of prediction rate with various experimental factors. By feeding a number of sample images of different people which serve as experimental subjects. The machine is first trained to generate features for each person among the testing subjects. Then, a new image was tested against the “learned” data and be labeled as one of the subjects. With experimental data analysis, the Eigenfaces method was determined to have the highest prediction rate of the three algorithms tested. The Local Binary Pattern Histogram (LBP) was found to have the lowest prediction rate. Finally, LBP was selected for the algorithm improvement. In this project, LBP was improved by identifying the most significant regions of the histograms for each person in training time. The weights of each region are assigned depending on the gray scale contrast. At recognition time, given a new face, different weights are assigned to different regions to increase prediction rate and also speed up the real time recognition. The experimental results confirmed the performance improvement.

This thesis focuses on the development of an open source software capable of automatic face detection in an image captured by a thermal camera, followed by a temperature measuring. This software is supposed to aid in the COVID-19 pandemics. The developed software is independent of used thermal camera. In this thesis, I am using TIM400 thermal camera. The implementation of the face detection was achieved by an OpenCV module. The methods tested were Template Matching, Eigen Faces, and Cascade Classifier. The last-mentioned had the best results, hence was used in the final version of the software. Cascade Classifier is looking for the eyes and their surrounding area in the image, allowing the software to subsequently measure the temperature on the surface of one's forehead. One can therefore be wearing a face mask or a respirator safely. The temperature measuring works in real time and the software is able to capture several people at once. It then keeps a record of the temperature of each measured individual as well as the time of the measurement. The software as a whole is a part of an installation file compatible with the Windows operating system. The functionality of this software was tested – the video recordings are included in this thesis.