Аннотация:
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.
