Abstract

In this work, we propose a novel model-based on a new Deep Hybrid Descriptor learning called DeepGLBSIF (Gabor Local Binarized Statistical Image Feature) for effective extraction and over-complete features in multilayer hierarchy. The typology of our methodology is the same as that of Convolutional Neural Network (CNN) which is one of the intensively-applied deep learning architectures. This field was developed due to: (i) end-to-end learning of the process utilizing a convolutional neural network (CNN), and (ii) the presence of very wide training databases. Our method allows improving the use of the interactions between global and local features for the model, which allowed providing effective and discriminating representations. In our study, the trainable kernels were substituted by our hybrid descriptor GLBSIF. Thus, the developed DeepGLBSIF architecture was efficiently and simply constructed and learned for Face Verification in the Wild. Finally, the classification process was carried out by applying distance measure Cosine and Support Vector Machine (SVM). Our experiments were performed on three large, real-world face datasets: LFW, PubFig and VGGface2. Experimental results demonstrate that our DeepGLBSIF approach provided competitive performance, compared to the others presented in state-of-the-art based on the LFW dataset for facial verification. A public CASIA-WebFace database was utilized in the training step of the introduced approach.

中文翻译：

---

使用深度学习方法在野外进行面部验证的高效混合描述符

摘要

在这项工作中，我们提出了一个基于名为DeepGLBSIF（Gabor局部二值化统计图像特征）的新深度混合描述符学习的新颖模型，用于多层层次结构中的有效提取和过度完成特征。我们方法的类型与卷积神经网络（CNN）相同，后者是深度应用的深度学习架构之一。该领域的开发是由于：（i）使用卷积神经网络（CNN）进行过程的端到端学习，以及（ii）存在非常广泛的培训数据库。我们的方法允许改进模型的全局和局部特征之间的交互作用，从而可以提供有效且具有区别性的表示形式。在我们的研究中，可训练内核被我们的混合描述符GLBSIF取代。从而，开发的DeepGLBSIF架构可以高效，简单地构建和学习，以用于野外人脸验证。最后，通过应用距离测量余弦和支持向量机（SVM）进行分类过程。我们的实验是在三个大型的真实人脸数据集上进行的：LFW，PubFig和VGGface2。实验结果表明，与基于LFW数据集进行面部验证的最新技术相比，我们的DeepGLBSIF方法具有竞争优势。引入的方法的培训步骤中使用了公共的CASIA-WebFace数据库。我们的实验是在三个大型的真实人脸数据集上进行的：LFW，PubFig和VGGface2。实验结果表明，与基于LFW数据集进行面部验证的最新技术相比，我们的DeepGLBSIF方法具有竞争优势。引入的方法的培训步骤中使用了公共的CASIA-WebFace数据库。我们的实验是在三个大型的真实人脸数据集上进行的：LFW，PubFig和VGGface2。实验结果表明，与基于LFW数据集进行面部验证的最新技术相比，我们的DeepGLBSIF方法具有竞争优势。引入的方法的培训步骤中使用了公共的CASIA-WebFace数据库。