Face Identification Using Eigenfaces and LBPH : A Comparative Study

Sammanfattning: 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.