Применение динамической аллокации на отображаемой памяти для обработки больших облаков точек в библиотеке PCL
Research in face detection has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing detection in constrained environments, the current face detection systems achieve very high accuracies on large-scale unconstrained face datasets. While upcoming algorithms continue to achieve improved performance, a majority of the face detection systems are susceptible to failure under disguise variations, one of the most challenging covariate of face detection. In this article, we propose a method for disguised face detection. The method consists of three stages. The first stage detects a face. The second one finds parts of the face on an output from the first stage. The third stage determines whether the face is disguised. Experiments show that our method can detect disguised faces in real time under the complex background and achieve acceptable disguised face detection rate.
Disguised face detection, convolutional neural networks, face parts, computer vision, neural networks, machine learning, face detection.