We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large and not known during training, and where the number of training samples for a single category is very small. The idea is to learn a function that maps input patterns into a target space such that the ¢ ¤£ norm in the target space approximates the "semantic" distance in the input space. The method is applied to a face verification task. The learning process minimizes a discriminative loss function that drives the similarity metric to be small for pairs of faces from the same person, and large for pairs from different persons. The mapping from raw to the target space is a convolutional network whose architecture is designed for robustness to geometric distortions. The system is tested on the Purdue/AR face database which has a very high degree of variability in the pose, lighting, expression, position, and artificial occlusions such as dark glasses and obscuring scarves.

Energy-Based Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables and finding configurations of the remaining variables that minimize the energy. Learning consists in finding an energy function in which observed configurations of the variables are given lower energies than unobserved ones. The EBM approach provides a common theoretical framework for many learning models, including traditional discriminative and generative approaches, as well as graph-transformer networks, conditional random fields, maximum margin Markov networks, and several manifold learning methods.Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of non-probabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches.

Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques.Real-life document recognition systems are composed of multiple modules including eld extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure.Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the exibility of Graph Transformer Networks.A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day.

In modern face recognition, the conventional pipeline consists of four stages: detect ⇒ align ⇒ represent ⇒ classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

The accuracy of k-nearest neighbor (kNN) classification depends significantly on the metric used to compute distances between different examples. In this paper, we show how to learn a Mahalanobis distance metric for kNN classification from labeled examples. The Mahalanobis metric can equivalently be viewed as a global linear transformation of the input space that precedes kNN classification using Euclidean distances. In our approach, the metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin. As in support vector machines (SVMs), the margin criterion leads to a convex optimization based on the hinge loss. Unlike learning in SVMs, however, our approach requires no modification or extension for problems in multiway (as opposed to binary) classification. In our framework, the Mahalanobis distance metric is obtained as the solution to a semidefinite program. On several data sets of varying size and difficulty, we find that metrics trained in this way lead to significant improvements in kNN classification. Sometimes these results can be further improved by clustering the training examples and learning an individual metric within each cluster. We show how to learn and combine these local metrics in a globally integrated manner.

横梁面部识别（CFR）旨在识别个体，其中比较面部图像源自不同的感测模式，例如红外与可见的。虽然CFR由于与模态差距相关的面部外观的显着变化，但CFR具有比经典的面部识别更具挑战性，但它在具有有限或挑战的照明的场景中，以及在呈现攻击的情况下，它是优越的。与卷积神经网络（CNNS）相关的人工智能最近的进展使CFR的显着性能提高了。由此激励，这项调查的贡献是三倍。我们提供CFR的概述，目标是通过首先正式化CFR然后呈现具体相关的应用来比较不同光谱中捕获的面部图像。其次，我们探索合适的谱带进行识别和讨论最近的CFR方法，重点放在神经网络上。特别是，我们提出了提取和比较异构特征以及数据集的重新访问技术。我们枚举不同光谱和相关算法的优势和局限性。最后，我们讨论了研究挑战和未来的研究线。

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.

可见光面图像匹配是跨模型识别的具有挑战性的变化。挑战在于，可见和热模式之间的较大的模态间隙和低相关性。现有方法采用图像预处理，特征提取或常见的子空间投影，它们本身是独立的问题。在本文中，我们提出了一种用于交叉模态面部识别的端到端框架。该算法的旨在从未处理的面部图像学习身份鉴别特征，并识别跨模态图像对。提出了一种新颖的单元级丢失，用于在丢弃模态信息时保留身份信息。另外，提出用于将图像对分类能力集成到网络中的跨模判位块。所提出的网络可用于提取无关的矢量表示或测试图像的匹配对分类。我们对五个独立数据库的跨型号人脸识别实验表明，该方法实现了对现有最先进的方法的显着改善。

The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available. A prototypical example of this is the one-shot learning setting, in which we must correctly make predictions given only a single example of each new class.In this paper, we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs. Once a network has been tuned, we can then capitalize on powerful discriminative features to generalize the predictive power of the network not just to new data, but to entirely new classes from unknown distributions. Using a convolutional architecture, we are able to achieve strong results which exceed those of other deep learning models with near stateof-the-art performance on one-shot classification tasks.

Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits "natural" variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.

近年来，围面识别被制定为有价值的生物识别方法，特别是在野生环境中（例如，由于Covid-19大流行导致的遮阳面），其中面部识别可能不适用。本文提出了一种名为基于属性的深周相识别（ADPR）的新的深周围识别框架，其预测软生物学测量，并将预测结合到周边识别算法中，以确定具有高精度的围绕围绕围绕图像的标识。我们提出了一个端到端的框架，它使用了几个共享卷积神经网络（CNN）层（公共网络），其输出馈送两个单独的专用分支（模态专用层）;第一分支在第二分支预测软管生物识别技术的同时分类周边图像。接下来，来自这两个分支的特征融合在一起以获得最终的周边识别。所提出的方法与现有方法不同，因为它不仅使用共享的CNN特征空间来共同培训这两个任务，而且还融合了预测的软生物识别功能，具有训练步骤中的周边特征，以提高整体周边识别性能。我们的建议模型使用四个不同的公共数据集进行了广泛的评估。实验结果表明，基于软生物识别的外观识别方法优于野生环境中的其他最先进的方法。

随着面部生物识别技术的广泛采用，在自动面部识别（FR）应用中区分相同的双胞胎和非双胞胎外观相似的问题变得越来越重要。由于同卵双胞胎和外观相似的面部相似性很高，因此这些面对对面部识别工具表示最困难的病例。这项工作介绍了迄今为止汇编的最大的双胞胎数据集之一，以应对两个挑战：1）确定相同双胞胎和2）的面部相似性的基线度量和2）应用此相似性措施来确定多ppelgangers的影响或外观 - Alikes，关于大面部数据集的FR性能。面部相似性度量是通过深度卷积神经网络确定的。该网络经过量身定制的验证任务进行培训，旨在鼓励网络在嵌入空间中将高度相似的面对对组合在一起，并达到0.9799的测试AUC。所提出的网络为任何两个给定的面提供了定量相似性评分，并已应用于大规模面部数据集以识别相似的面对对。还执行了一个附加分析，该分析还将面部识别工具返回的比较分数以及提议网络返回的相似性分数。

面部变形攻击检测具有挑战性，并为面部验证系统带来了具体和严重的威胁。此类攻击的可靠检测机制已通过强大的跨数据库协议和未知的变形工具进行了测试，这仍然是一项研究挑战。本文提出了一个框架，遵循了几次射击学习方法，该方法使用三胞胎 - 硬性损坏共享基于暹罗网络的图像信息，以应对变形攻击检测并增强聚类分类过程。该网络比较了真正的或潜在的变形图像与变形和真正的面部图像的三胞胎。我们的结果表明，这个新的网络将数据点群集成，并将它们分配给类，以便在跨数据库方案中获得较低的相等错误率，仅共享来自未知数据库的小图像编号。几乎没有学习的学习有助于增强学习过程。使用FRGCV2训练并使用FERET和AMSL开放式数据库测试的跨数据库的实验结果将BPCer10使用RESNET50和5.50％的MobileNETV2从43％降低到4.91％。

机器学习模型通常会遇到与训练分布不同的样本。无法识别分布（OOD）样本，因此将该样本分配给课堂标签会显着损害模​​型的可靠性。由于其对在开放世界中的安全部署模型的重要性，该问题引起了重大关注。由于对所有可能的未知分布进行建模的棘手性，检测OOD样品是具有挑战性的。迄今为止，一些研究领域解决了检测陌生样本的问题，包括异常检测，新颖性检测，一级学习，开放式识别识别和分布外检测。尽管有相似和共同的概念，但分别分布，开放式检测和异常检测已被独立研究。因此，这些研究途径尚未交叉授粉，创造了研究障碍。尽管某些调查打算概述这些方法，但它们似乎仅关注特定领域，而无需检查不同领域之间的关系。这项调查旨在在确定其共同点的同时，对各个领域的众多著名作品进行跨域和全面的审查。研究人员可以从不同领域的研究进展概述中受益，并协同发展未来的方法。此外，据我们所知，虽然进行异常检测或单级学习进行了调查，但没有关于分布外检测的全面或最新的调查，我们的调查可广泛涵盖。最后，有了统一的跨域视角，我们讨论并阐明了未来的研究线，打算将这些领域更加紧密地融为一体。

These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools.

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.

这是一门专门针对STEM学生开发的介绍性机器学习课程。我们的目标是为有兴趣的读者提供基础知识，以在自己的项目中使用机器学习，并将自己熟悉术语作为进一步阅读相关文献的基础。在这些讲义中，我们讨论受监督，无监督和强化学习。注释从没有神经网络的机器学习方法的说明开始，例如原理分析，T-SNE，聚类以及线性回归和线性分类器。我们继续介绍基本和先进的神经网络结构，例如密集的进料和常规神经网络，经常性的神经网络，受限的玻尔兹曼机器，（变性）自动编码器，生成的对抗性网络。讨论了潜在空间表示的解释性问题，并使用梦和对抗性攻击的例子。最后一部分致力于加强学习，我们在其中介绍了价值功能和政策学习的基本概念。

许多最近的作品通过基于参数模型聚集了相同的身份的形状参数并将不同人的形状参数聚集在一起（例如，3D可变模型（3DMMS））来重建独特的3D面形状。然而，尽管使用这些形状参数的面部识别任务中的高精度，但是从那些参数重建的面部形状的视觉辨别是不令人满意的。以下研究尚未回答以下研究问题：做差异的形状参数保证所代表的3D面形状的视觉歧视吗？本文分析了形状参数与重建形状几何之间的关系，提出了一种新颖的形状相同感知正则化（SIR）损耗的形状参数，旨在增加形状参数和形状几何域中的辨别性。此外，为了应对包含地标和身份注释的缺乏培训数据，我们提出了一种网络结构和相关的培训策略，以利用包含身份或地标标签的混合数据。我们将我们的方法与现有方法进行比较重建误差，视觉区分性和形状参数的面部识别准确性。实验结果表明，我们的方法优于最先进的方法。

在这项研究中，在使用Flickr-Faces-HQ和SpangeFaces数据集生成的遮罩与揭露面上的面部识别，我们报告了由Pandemics的掩模穿着掩盖穿着的识别性能的36.78％劣化，特别是在边境检查点情景中。在跨光谱域中的高级深度学习方法，我们取得了更好的性能并降低了1.79％的劣化。

基于全面的生物识别是一个广泛的研究区域。然而，仅使用部分可见的面，例如在遮盖的人的情况下，是一个具有挑战性的任务。在这项工作中使用深卷积神经网络（CNN）来提取来自遮盖者面部图像的特征。我们发现，第六和第七完全连接的层，FC6和FC7分别在VGG19网络的结构中提供了鲁棒特征，其中这两层包含4096个功能。这项工作的主要目标是测试基于深度学习的自动化计算机系统的能力，不仅要识别人，还要对眼睛微笑等性别，年龄和面部表达的认可。我们的实验结果表明，我们为所有任务获得了高精度。最佳记录的准确度值高达99.95％，用于识别人员，99.9％，年龄识别的99.9％，面部表情（眼睛微笑）认可为80.9％。