Application of a Ten-Variate Prediction Ellipsoid for Normalized Data and Machine Learning Algorithms for Face Recognition

Abstract

Face recognition plays a pivotal role in enhancing security, improving efficiency, and enabling innovative applications across diverse industries, making it an indispensable tool in today's world. This study presents a comparative investigation between prediction ellipsoid and machine learning algorithms, such as a one-class support vector machine, isolation forest, and an autoencoder based on a neural network, in the context of face recognition. The construction of a prediction ellipsoid is based on the assumption of a multivariate normal distribution of the data, however, in real-world scenarios, the data often deviates from this assumption and may exhibit a non-Gaussian distribution. To mitigate this, the dataset underwent normalization via the multivariate Box-Cox transformation, facilitating the development of a decision rule based on a ten-variate prediction ellipsoid for the normalized data. This approach yielded notable enhancements in accuracy and method robustness. All the developed models have shown good efficiency in their respective performances. However, the application of the multivariate Box-Cox normalizing transformation significantly enhanced the performance of the prediction ellipsoid. As a result, the ten-variable prediction ellipsoid for normalized data has demonstrated superior efficiency compared to the other models. The study also highlights the critical role of feature dimensionality in face recognition, emphasizing the necessity of expanding the number of features and utilizing more complex models to achieve optimal performance.