复杂背景下面部表情分类的两级算法

K. Sannikov, A. A. Bashlikov, A. Druki
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引用次数: 2

摘要

本研究的相关性是由于需要设计算法来提高复杂背景图像的人脸检测和情绪识别效率。目的:开发算法和软件系统,以提高人脸检测和人脸表情分类的效率,在复杂的背景下,在存在异物的情况下,在变化的照明,噪声和不同的失真情况下。实验调查将执行的效率实现算法和比较其现有的类似物。研究结果:提出了基于Viola Jones方法的人脸检测算法,用于复杂背景图像的人脸检测。提出了一种具有原始结构的卷积神经网络(CNN)面部表情分类模型。给出了测试和训练参数的描述,并与现有的类似物进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-level algorithm of facial expressions classification on complex background
The relevance of this study is stipulated by the necessity of designing algorithms allowing to improve the efficiency of human face detection and emotions recognition on images with complex background. Purpose: Development of algorithms and software system allowing to improve the efficiency of human face detection and in addition facial expression classification on images with complex background, in the presence of foreign objects, changing illumination, noise and different distortions. Experimental investigations to be performed into the efficiency of implemented algorithms and comparison to their existing analogs. Findings: Face detection algorithm based on Viola Jones method — is proposed to face detection on images with complex background. The model of convolutional neural network (CNN) with original structure is proposed for facial expression classification. The description of testing and training parameters, as well as comparisons with existing analogues are presented.
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