人脸识别模块的实现外文文献翻译、中英文翻译、外文翻译

人脸识别模块的实现外文文献翻译、中英文翻译、外文翻译,识别,模块,实现,外文,文献,翻译,中英文 译文： 人脸识别模块的实现 尾形克彦 传统的人脸识别技术主要是基于可见光图像的人脸识别，这也是人们熟悉的识别方式，已有30多年的研发历史。但这种方式有着难以克服的缺陷，尤其在环境光照发生变化时，识别效果会急剧下降，无法满足实际系统的需要。解决光照问题的方案有三维图像人脸识别，和热成像人脸识别。但这两种技术还远不成熟，识别效果不尽人意。近年来迅速发展起来的一种解决方案是基于主动近红外图像的多光源人脸识别技术。它可以克服光线变化的影响，已经取得了卓越的识别性能，在精度、稳定性和速度方面的整体系统性能超过三维图像人脸识别。这项技术在近两三年发展迅速，使人脸识别技术逐渐走向实用化。 人脸与人体的其它生物特征（指纹、虹膜等）一样与生俱来，它的唯一性和不易被复制的良好特性为身份鉴别提供了必要的前提，与其它类型的生物识别比较人脸识别具有如下特点： 非强制性：用户不需要专门配合人脸采集设备，几乎可以在无意识的状态下就可获取人脸图像，这样的取样方式没有“强制性”； 非接触性：用户不需要和设备直接接触就能获取人脸图像； 并发性：在实际应用场景下可以进行多个人脸的分拣、判断及识别； 除此之外，还符合视觉特性：“以貌识人”的特性，以及操作简单、结果直观、隐蔽性好等特点。 人脸识别系统主要包括四个组成部分，分别为：人脸图像采集及检测、人脸图像预处理、人脸图像特征提取以及匹配与识别。 1.人脸图像采集及检测 人脸图像采集：不同的人脸图像都能通过摄像镜头采集下来，比如静态图像、动态图像、不同的位置、不同表情等方面都可以得到很好的采集。当用户在采集设备的拍摄范围内时，采集设备会自动搜索并拍摄用户的人脸图像。 人脸检测：人脸检测在实际中主要用于人脸识别的预处理，即在图像中准确标定出人脸的位置和大小。人脸图像中包含的模式特征十分丰富，如直方图特征、颜色特征、模板特征、结构特征及Haar特征等。人脸检测就是把这其中有用的信息挑出来，并利用这些特征实现人脸检测。 主流的人脸检测方法基于以上特征采用Adaboost学习算法，Adaboost算法是一种用来分类的方法，它把一些比较弱的分类方法合在一起，组合出新的很强的分类方法。 人脸检测过程中使用Adaboost算法挑选出一些最能代表人脸的矩形特征(弱分类器)，按照加权投票的方式将弱分类器构造为一个强分类器，再将训练得到的若干强分类器串联组成一个级联结构的层叠分类器，有效地提高分类器的检测速度。 2.人脸图像预处理 人脸图像预处理：对于人脸的图像预处理是基于人脸检测结果，对图像进行处理并最终服务于特征提取的过程。系统获取的原始图像由于受到各种条件的限制和随机干扰，往往不能直接使用，必须在图像处理的早期阶段对它进行灰度校正、噪声过滤等图像预处理。对于人脸图像而言，其预处理过程主要包括人脸图像的光线补偿、灰度变换、直方图均衡化、归一化、几何校正、滤波以及锐化等。 3.人脸图像特征提取 人脸图像特征提取：人脸识别系统可使用的特征通常分为视觉特征、像素统计特征、人脸图像变换系数特征、人脸图像代数特征等。人脸特征提取就是针对人脸的某些特征进行的。人脸特征提取，也称人脸表征，它是对人脸进行特征建模的过程。人脸特征提取的方法归纳起来分为两大类：一种是基于知识的表征方法；另外一种是基于代数特征或统计学习的表征方法。 基于知识的表征方法主要是根据人脸器官的形状描述以及他们之间的距离特性来获得有助于人脸分类的特征数据，其特征分量通常包括特征点间的欧氏距离、曲率和角度等。人脸由眼睛、鼻子、嘴、下巴等局部构成，对这些局部和它们之间结构关系的几何描述，可作为识别人脸的重要特征，这些特征被称为几何特征。基于知识的人脸表征主要包括基于几何特征的方法和模板匹配法。 4.人脸图像匹配与识别 人脸图像匹配与识别：提取的人脸图像的特征数据与数据库中存储的特征模板进行搜索匹配，通过设定一个阈值，当相似度超过这一阈值，则把匹配得到的结果输出。人脸识别就是将待识别的人脸特征与已得到的人脸特征模板进行比较，根据相似程度对人脸的身份信息进行判断。这一过程又分为两类：一类是确认，是一对一进行图像比较的过程，另一类是辨认，是一对多进行图像匹配对比的过程。 国籍：美国 出处：《人脸识别技术》普伦蒂斯霍尔出版社 原文： Face recognition technology Katsuhiko Ogata Traditional face recognition technology is mainly based on visible image face recognition, which is also a familiar way of recognition, has more than 30 years of research and development history. However, this method has some defects that are difficult to overcome. Especially when the ambient illumination changes, the recognition effect will drop sharply, which cannot meet the needs of the actual system. Solutions to solve the lighting problem include 3D image face recognition, and thermal image face recognition. However, these two technologies are still far from mature and the recognition effect is not satisfactory. In recent years, a rapidly developed solution is multi-light source face recognition technology based on active near-infrared image. It can overcome the impact of light changes, has achieved excellent recognition performance, in accuracy, stability and speed of the overall system performance than 3D image face recognition. This technology has developed rapidly in the past two or three years, making face recognition technology increasingly practical. Face and other biological characteristics of the human body (fingerprint, iris, etc.) as innate, its uniqueness and not easy to be copied good characteristics for identity identification provides the necessary premise, compared with other types of biometric face recognition has the following characteristics: Non-mandatory: the user does not need to specially cooperate with the face acquisition equipment, can almost in the unconscious state can obtain the face image, such a sampling method is not "mandatory"; Non-contact: users can obtain face images without direct contact with the device; Concurrency: in the actual application scenarios can be multiple face sorting, judgment and recognition; In addition, it also conforms to the visual characteristics: the characteristics of "knowing people by appearance", and the characteristics of simple operation, intuitive results, good concealment and so on. Face recognition system mainly includes four parts, respectively: face image acquisition and detection, face image preprocessing, face image feature extraction and matching and recognition. 1. Face image acquisition and detection Face image acquisition: different face images can be collected through the camera lens, such as static images, dynamic images, different positions, different expressions and other aspects can be very good collection. When the user is within the shooting range of the acquisition device, the acquisition device will automatically search and take the user's face image. Face detection: Face detection in practice is mainly used for face recognition pretreatment, that is, in the image to accurately calibrate the position and size of the face. Face image contains a very rich pattern features, such as histogram features, color features, template features, structural features and Haar features. Face detection is to pick out the useful information, and use these features to achieve face detection. Mainstream face detection methods adopt Adaboost learning algorithm based on the above features. Adaboost algorithm is a method used for classification. It combines some relatively weak classification methods together to create a new strong classification method. Face detection in the process of using Adaboost algorithm can pick out some of the most representative characteristics of face of the rectangular (weak classifier), according to the weighted voting to weak classifier structure for a strong classifier, and then obtained a number of strong classifier training series consists of a cascade structure cascade classifier, effectively improve the detection speed of classifier. 2 face image preprocessing Face image preprocessing: the face image preprocessing is based on the face detection results, the image processing and ultimately serve the process of feature extraction. The original image obtained by the system can not be used directly because of the limitation of various conditions and random interference. It must be preprocessed in the early stage of image processing, such as gray correction and noise filtering. For face image, its preprocessing process mainly includes face image light compensation, gray transformation, histogram equalization, normalization, geometric correction, filtering and sharpening. 3. Face image feature extraction Face image feature extraction: face recognition system can be used features are usually divided into visual features, pixel statistical features, face image transformation coefficient features, face image algebraic features, etc.. Face feature extraction is aimed at some features of the face. Face feature extraction, also known as face representation, is the process of face feature modeling. Facial feature extraction methods can be summarized into two categories: one is knowledge-based characterization method; The other method is based on algebraic features or statistical learning. The knowledge-based representation method is mainly based on the shape description of the face organs and the distance between them to obtain the feature data helpful to face classification, the feature components usually include the Euclidean distance between the feature points, curvature and Angle, etc. The face is composed of eyes, nose, mouth, chin and other parts. The geometric description of these parts and the structural relationship between them can be used as an important feature to recognize the face. These features are called geometric features. Knowledge-based face representation mainly includes geometric feature-based method and template matching method. 4. Face image matching and recognition Face image matching and recognition: the extraction of face image feature data and the database stored in the feature template search matching, by setting a threshold, when the similarity exceeds this threshold, the matching results output. Face recognition is to recognize the face features and the face features template has been compared, according to the degree of similarity on the face of the identity information to judge. This process is divided into two categories: one is confirmation, is a one-to-one image comparison process, the other is recognition, is a one-to-many image matching comparison process. Nationality: USA Source: Face recognition technology 译文： 关于实现人脸算法识别 人脸识别系统包括图像摄取、人脸定位、图像预处理、以及人脸识别（身份确认或者身份查找）。系统输入一般是一张或者一系列含有未确定身份的人脸图像，以及人脸数据库中的若干已知身份的人脸图象或者相应的编码，而其输出则是一系列相似度得分，表明待识别的人脸的身份。 人脸识别算法分类 基于人脸特征点的识别算法（Feature-based recognition algorithms）。 基于整幅人脸图像的识别算法（Appearance-based recognition algorithms）。 基于模板的识别算法（Template-based recognition algorithms）。 利用神经网络进行识别的算法（Recognition algorithms using neural network）。 神经网络识别 基于光照估计模型理论，提出了基于Gamma灰度矫正的光照预处理方法,并且在光照估计模型的基础上，进行相应的光照补偿和光照平衡策略。 优化的形变统计校正理论基于统计形变的校正理论，优化人脸姿态；强化迭代理论。强化迭代理论是对DLFA人脸检测算法的有效扩展； 独创的实时特征识别理论 该理论侧重于人脸实时数据的中间值处理，从而可以在识别速率和识别效能之间，达到最佳的匹配效果。 人脸识别的优势在于其自然性和不被被测个体察觉的特点。 虹膜识别 所谓自然性，是指该识别方式同人类（甚至其他生物）进行个体识别时所利用的生物特征相同。例如人脸识别，人类也是通过观察比较人脸区分和确认身份的，另外具有自然性的识别还有语音识别、体形识别等，而指纹识别、虹膜识别等都不具有自然性，因为人类或者其他生物并不通过此类生物特征区别个体。 不被察觉的特点对于一种识别方法也很重要，这会使该识别方法不令人反感，并且因为不容易引起人的注意而不容易被欺骗。人脸识别具有这方面的特点，它完全利用可见光获取人脸图像信息，而不同于指纹识别或者虹膜识别，需要利用电子压力传感器采集指纹，或者利用红外线采集虹膜图像，这些特殊的采集方式很容易被人察觉，从而更有可能被伪装欺骗。 人脸识别被认为是生物特征识别领域甚至人工智能领域最困难的研究课题之一。人脸识别的困难主要是人脸作为生物特征的特点所带来的。 相似性 人脸类似性 不同个体之间的区别不大，所有的人脸的结构都相似，甚至人脸器官的结构外形都很相似。这样的特点对于利用人脸进行定位是有利的，但是对于利用人脸区分人类个体是不利的。 易变性 人脸的外形很不稳定，人可以通过脸部的变化产生很多表情，而在不同观察角度，人脸的视觉图像也相差很大，另外，人脸识别还受光照条件（例如白天和夜晚，室内和室外等）、人脸的很多遮盖物（例如口罩、墨镜、头发、胡须等）、年龄等多方面因素的影响。 在人脸识别中，第一类的变化是应该放大而作为区分个体的标准的，而第二类的变化应该消除，因为它们可以代表同一个个体。通常称第一类变化为类间变化（inter-class difference），而称第二类变化为类内变化（intra-class difference）。对于人脸，类内变化往往大于类间变化，从而使在受类内变化干扰的情况下利用类间变化区分个体变得异常困难。 国籍：美国 出处：《关于人脸识别算法的实现》普伦蒂斯霍尔出版社 原文： On the realization of face algorithm recognition Katsuhiko Ogata Face recognition system includes image ingestion, face location, image preprocessing, and face recognition (identity confirmation or identity search). The system input is generally a piece or a series of face images containing unidentified identity, as well as a number of known identity face images in the face database or the corresponding coding, and its output is a series of similarity scores, indicating the identity of the face to be recognized. Face recognition algorithm classification Feature-based recognition algorithms based on face Feature points. Appearance-based recognition algorithms for whole face images. Template-Based Recognition Algorithms. Recognition algorithms using neural network. Neural network recognition Based on the theory of light estimation model, a light preprocessing method based on Gamma gray correction is proposed, and the corresponding light compensation and light balance strategies are carried out on the basis of the light estimation model. Based on the correction theory of statistical deformation, the face pose is optimized. Reinforce iteration theory. Enhanced iteration theory is an effective extension of DLFA face detection algorithm. Original real - time feature recognition theory The theory focuses on the intermediate value processing of real-time face data, so that the best matching effect can be achieved between the recognition rate and the recognition efficiency. The advantage of face recognition lies in its nature and the characteristics of not being detected by the individual. Iris recognition The so-called naturalness means that the identification method is the same as the biological characteristics used by human beings (or even other organisms) for individual identification. For example, face recognition, human is also through the comparison of face discrimination and identification, in addition to natural recognition and voice recognition, shape recognition, and fingerprint recognition, iris recognition, etc., are not natural, because humans or other organisms do not distinguish individuals through such biological characteristics. The quality of being unobservable is also important for a method of recognition, which makes the method not objectionable and, because it is not easy to draw attention to, less susceptible to deception. Face recognition has the characteristics of this aspect, it fully using visible light for face image information, and is different from the fingerprint recognition or iris recognition, need to use electronic pressure sensor fingerprinted, or by using infrared acquisition iris image, the mode of these special collection is very easy to detect, thus is more likely to be deceived by camouflage. Face recognition is considered to be one of the most difficult research topics in the field of biometric recognition and even artificial intelligence. The difficulty of face recognition is mainly caused by the characteristics of face as biological features. similarity Facial similarity There is little difference between individuals. All faces are structurally similar, and even the facial organs are structurally similar. This feature is beneficial to the use of human face localization, but it is unfavorable to the use of human face to distinguish human individuals. variability Facial appearance is not very stable, people can through the change of face a lot of expressions, and in different viewing Angle, the face of visual images also vary widely, in addition, face recognition is affected by light conditions (such as day and night, indoor and outdoor, etc.), face a lot of cover (for example, masks and sunglasses, hair, beard, etc.), the influence of various factors such as age. In face recognition, the first type of changes should be magnified as a standard to distinguish individuals, while the second type of changes should be eliminated because they can represent the same individual. Usually called the first type of change for inter-class change (inter-class difference), and called the second type of change for intra-class change (intra-class difference). For faces, the intra-class variation is often greater than the inter-class variation, which makes it extremely difficult to distinguish individuals by inter-class variation under the interference of intra-class variation. Nationality: USA Source: On the realization of face algorithm recognition