用于测谎的混合机器学习模型

Rupali J Dhabarde, D. V. Kodawade, Sheetal Zalte
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引用次数: 0

摘要

从照片中识别一个人的技术是面部识别。由于其在多个领域的广泛应用,近年来引起了计算机视觉领域(网络安全、犯罪案件和生物识别)众多研究人员的关注。该技术的操作是基于使用PCA, ICA, LDA等方法从输入图像中提取特征。将其与另一图像中的其他图像进行比较,以验证或断言个人身份。通过这项工作,我们将CNN和SVM技术的合并应用于两个人脸数据集,这些数据集将在基于机器学习的方法中分成两组。我们使用我们积累的数据集评估了不同的基于机器学习的测谎仪。我们的研究结果表明,CNN与SVM任务相结合的准确率高达58%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Machine Learning Model for Lie-Detection
A technique for recognizing a person from his photograph is facial recognition. Due to its extensive range of applications in several fields, it has drawn the attention of numerous researchers in the field of computer vision in recent years (Cyber security, crime cases, and biometrics). This technology's operation is based on the extraction of features from an input picture using methods like PCA, ICA, LDA etc. After comparing them with others from another image to verify or assert an individual's identification. Via this work, we applied amalgamation of CNN and SVM techniques to two face datasets that will be split into two groups in a machine learning-based methodology. We assessed different machine learning-based lie detectors using our amassed dataset. Our findings demonstrate that combined CNN with SVM task achieved accuracy up to 58%.
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