基于支持向量机的计算机视觉数据集深度q残差分析

Rahama Salman
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引用次数: 0

摘要

基于视觉的人机界面用于自动识别人类情绪。所使用的图像处理技术包括用于眼睛检测的网络相机。外观跟踪方法(ABT)用于人脸识别,K均值最近邻(K-NN)用于眼睛检测。离散小波变换和离散余弦变换适用于眼睛特征的提取,支持向量机用于眼睛表情的分类。眼睛表情的分类包括愤怒、恐惧、快乐、厌恶、中性和悲伤。实验结果表明,该方法对人脸表情的识别精度较高。
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DeepQ Residue Analysis of Computer Vision Dataset using Support Vector Machine
A vision-based human computer interface is used to automatically recognize human mood. Image processing techniques used include a web camera for eye detection. Appearance tracking method (ABT) is measured face identification and K means Nearest Neighbor (K-NN) is used for eye detection. DWT - Discrete Wavelet Transform and DCT - Discrete Cosine Transform are suitable to extract features of eye and SVM is used to classify eye expressions. Classification of eye expressions includes anger, fear, happiness, disgust, neutral and sad. Experimental results confirm that the proposed method recognized facial expressions with higher accuracy.
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来源期刊
Journal of Internet Services and Information Security
Journal of Internet Services and Information Security Computer Science-Computer Science (miscellaneous)
CiteScore
3.90
自引率
0.00%
发文量
0
审稿时长
8 weeks
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