关键词: 深度卷积神经网络（DCNN）, 面部特征点检测, 卷积层和池化层, 驾驶员面部朝向

Abstract:

This paper introduces the theory of face feature point detection, and proposes a solution based on Deep Convolutional Neural Network（DCNN） to solve the five facial feature points（eye, nose, mouth） prediction problem. By adding more convolutional layers to steadily increase the depth of the network, and using 3×3 convolutional filters in all layers, the parameters are effectively reduced, thus the problem of facial feature point detection is better solved. Then the homomorphism matrix of the plane formed by the corner of eyes and mouth and the plane when facing directly is calculated. Finally, the driver’s facial rotation angle is solved by the equivalent algorithm. Experimental results show that the detection accuracy of facial feature points is 97.96%, and the error of the algorithm in angle judgment is 1°-5°, which proves the effectiveness of the algorithm in the monitoring of distraction.

Key words: Deep Convolutional Neural Network（DCNN）, facial feature points detection, convolution and pooling, driver’s facial orientation