Сборники тезисов • Инженерные системы и приборостроение • Биометрические системы
Сборник тезисов докладов конгресса молодых ученых. Электронное издание. – СПб: Университет ИТМО, 2018.
Пример заполнения выходных данных:
Зено Б.., Матвеев Ю.Н. Face Validation Using Variational Autoencoder // Сборник тезисов докладов конгресса молодых ученых. Электронное издание [Электронный ресурс]. - Режим доступа: ссылка на страницу с тезисом, своб.
Face Validation Using Variational Autoencoder
УДК: 004.89
Аннотация:
In unconstrained facial images, large visual variations concerning pose, scale, the presence of occlusions, expressions and lighting usually cause difficulties in discriminating faces from the background accurately. As a result, some non-face regions are recognized as faces (false positive) and that influences the effectiveness of face detection algorithms which is characterized by low false positive (FP) rate, high detection rate and high speed of processing. In order to reduce these non-face regions (identification), we propose method using reconstruction error from variational autoencoder (VAE), which is a generative machine learning model. we train VAE to learn reconstructing faces that are close to its original input faces, then we measure the difference between the original input face and the reconstructed output to obtain the reconstruction error. Consequently, the regions resulting from faces detection algorithm with high reconstruction error are defined as false positives.