PDF(Pubmed)
Abstract：
UNASSIGNED: Developing efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks.
UNASSIGNED: We describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation.
UNASSIGNED: Overall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.
UNASSIGNED: We describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation.
UNASSIGNED: Overall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.
摘要：
■开发有效的方法来推断由众多表情组成的不同面孔之间的关系或在不同时间在同一面孔上的关系（例如，疾病进展）是影像学相关研究中的一个悬而未决的问题。在这项研究中,我们提出了一种新颖的面部特征提取方法，表征，基于经典计算机视觉和深度学习的识别，更具体地说,卷积神经网络。
■我们描述了名为FRetrAival（FRAI）的混合面部表征系统，它是GoogleNet和AlexNet神经网络(NN)模型的混合。通过FRAI网络分析的图像通过计算机视觉技术进行预处理，例如基于梯度的定向算法，该算法只能从任何类型的图片中提取人脸区域。使用对齐人脸数据集（AFD）来训练和测试用于提取图像特征的FRAI解决方案。野生(LFW)保持数据集中的标记面已用于外部验证。
■总的来说，与以前的技术相比，我们的方法通过产生最大精度，在k-最近邻(KNN)上显示出更好的结果，召回，F1和F2得分值(92.00、92.66、92.33和92.52%，分别)对于AFD和(每个变量为95.00%)对于LFW数据集，它们被用作训练和测试数据集。FRAI模型可能会用于医疗保健和犯罪学以及许多其他应用中，在这些应用中，快速识别特定识别目标的指纹等面部特征非常重要。
■我们描述了名为FRetrAival（FRAI）的混合面部表征系统，它是GoogleNet和AlexNet神经网络(NN)模型的混合。通过FRAI网络分析的图像通过计算机视觉技术进行预处理，例如基于梯度的定向算法，该算法只能从任何类型的图片中提取人脸区域。使用对齐人脸数据集（AFD）来训练和测试用于提取图像特征的FRAI解决方案。野生(LFW)保持数据集中的标记面已用于外部验证。
■总的来说，与以前的技术相比，我们的方法通过产生最大精度，在k-最近邻(KNN)上显示出更好的结果，召回，F1和F2得分值(92.00、92.66、92.33和92.52%，分别)对于AFD和(每个变量为95.00%)对于LFW数据集，它们被用作训练和测试数据集。FRAI模型可能会用于医疗保健和犯罪学以及许多其他应用中，在这些应用中，快速识别特定识别目标的指纹等面部特征非常重要。
