Abstract
Abstract. Today, possibilities of artificial intelligence allow us to see the emergence of autonomous cars. However, there are still many problems in this area at present. Often, such vehicles are “too slow to think”, are not able to reliably process data from video cameras in the event of reflections, glare, and there are also questions about the safety of such driving in difficult weather conditions or in heavy traffic. At the same time, the human factor plays a major role in accidents of driven vehicles. Many accidents involve driver fatigue, distraction, or even falling asleep. At the same time, it is potentially possible to monitor the state of a person behind the wheel by a video sequence received from a camera installed in the car's interior and registering the driver's face in video sequence. In this paper, the existing databases of images of faces and eyes are considered, and an algorithm is presented that detects the state of closed eyes based on Haar detectors and convolutional neural networks.
Highlights
Days one of the main applications for artificial intelligence, along with natural language processing and reinforcement learning, is computer vision
All considered methods can be classified according to the used features of objects into three groups (Kuznetsov, 2020): 1. Structural approach - describes objects as a system consisting of many interconnected elements, includes methods such as scale-invariant feature transform (SIFT), SURF (Speeded Up Robust Features), BRIEF, local binary pattern (LBP), histogram of oriented gradients (HOG), local phase quantization (LPQ)
The obtained characteristics for a network based on the VGG-19 architecture are significantly superior to a simple five-layer convolutional neural network trained from scratch
Summary
Days one of the main applications for artificial intelligence, along with natural language processing and reinforcement learning, is computer vision. One of the areas of application of computer vision algorithms is the detection, recognition and analysis of a person directly. Most often, this task comes down to detecting a face in the image and comparing this face with faces from the existing database. The faces of different people have many of the same features. In this case, a person's face can be used to detect parts of the face, such as nose, mouth, eyes, as well as to determine emotions expressed on the face
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