• Face Verification SDK

 

FACE VERIFICATION SDK
BIOMETRIC IDENTITY VERIFICATION FOR LARGE-SCALE HIGH-SECURITY APPLICATIONS
The Face Verification SDK is designed for integration of facial authentication into enterprise and consumer applications for mobile devices and PCs. The simple API of the library component helps to implement solutions like payment, e-services and all other apps that need enhanced security through biometric face recognition, while keeping their overall size small for easy deployment to millions of users.
Different liveness detection functionalities are included to implement anti-spoofing mechanism with the possibility of configuring the balance between security and usability of the application.

FACE VERIFICATION SDK
大规模高安全应用中的生物特征识别
Face Verification SDK是为将人脸识别集成到移动设备和PC的企业和消费者应用程序中而设计的。库组件的简单API有助于实现解决方案,如支付、电子服务和所有其他需要通过生物特征人脸识别来增强安全性的应用程序,同时将它们的整体规模保持在较小的范围内,可方便地部署到数百万用户。
采用不同的活性检测功能来实现防欺骗机制,实现了应用程序的安全性和可用性之间的平衡。

 

FEATURES AND CAPABILITIES

  • Compact library for deployment on mobile devices.
  • Based on VeriLook technology with millions of deployments worldwide.
  • Live face detection prevents spoofing.
  • Android, iOS, Microsoft Windows, Mac OS X and Linux supported.
  • Programming samples in multiple languages included.
  • Reasonable prices, flexible licensing and free customer support.

 

产品特点和功能

  • 用于在移动设备上部署的紧凑库。
  • 基于VeriLook技术,该技术在世界各地已部署了数以百万计应用。
  • 实时人脸检测可以防止欺骗。
  • 支持AndroidiOSMicrosoftWindowsMacOSXLinux
  • 包括多种语言的编程示例。
  • 合理的价格,灵活的许可和免费的客户支持。

The Face Verification SDK is intended for developing applications which perform end-user identity verification in mass scale systems like:

  • online banking and e-shops;
  • government e-services;
  • social networks and media sharing services.

The Face Verification SDK is based on the VeriLook algorithm, which provides advanced face localization, enrollment and matching using robust digital image processing algorithms based on deep neural networks. The SDK offers these features for large-scale identity verification systems:

  • Live face detection. A conventional face identification system can be tricked by placing a photo in front of the camera. Face Verification SDK is able to prevent this kind of security breach by determining whether a face in a video stream is "live" or a photograph. The liveness detection can be performed in passive mode, when the engine evaluates certain facial features, and in active mode, when the engine evaluates user's response to perform actions like blinking or head movements.
  • Face image quality determination. A quality threshold can be used during face enrollment to ensure that only the best quality face template will be stored into database.
  • Tolerance to face position. The Face Verification SDK allows head roll, pitch and yaw variation up to 15 degrees in each direction from the frontal position.
  • Multiple samples of the same face. Biometric template record can contain multiple face samples belonging to the same person. These samples can be enrolled from different sources and at different times, thus allowing improvement in matching quality. For example a person might be enrolled with and without beard or mustache, etc.
  • Features generalization mode. This mode generates the collection of the generalized face features from several images of the same subject. Then, each face image is processed, features are extracted, and the collections of features are analyzed and combined into a single generalized features collection, which is written to the database. This way, the enrolled feature template is more reliable and the face recognition quality increases considerably.

Face Verification SDK用于开发在大规模系统中执行终端用户身份验证的应用程序,如:

  • 网上银行和电子商店;
  • 政府电子服务;
  • 社交网络和媒体共享服务。

基于VeriLook算法的人脸验证SDK采用了基于深度神经网络的鲁棒数字图像处理算法,提供了先进的人脸定位、登录和匹配。SDK为大规模身份验证系统提供了以下特性:

  • 现场人脸检测传统的人脸识别系统可以通过在摄像机前放置一张照片来欺骗。FaceVersionSDK能够通过确定视频流中的脸是“活体”还是一张照片来防止这种安全漏洞。这个活性检测可以在被动模式下执行,由引擎评估某些面部特征;在主动模式下,则由引擎评估用户的响应(执行诸如眨眼或头部移动之类的动作)。
  • 人脸图像质量测定在人脸注册过程中可以使用质量阈值,以确保只将最佳质量的人脸模板存储到数据库中。
  • 对面部位置的容忍度脸验证SDK允许头部滚动,俯仰和偏航变化高达15度在每个方向从正面的位置。
  • 同一张脸的多个样本生物识别模板记录可以包含属于同一人的多个人脸样本。这些样品可以从不同的来源和不同的时间登记,从而提高匹配质量。例如,一个人可能注册时有胡子或胡须,等等。
  • 特征归一化模式该模式从同一主题的多幅图像中生成广义人脸特征的集合。然后,对每个人脸图像进行处理,提取特征,并将特征集合分析组合成一个单一的广义特征集合,并将其写入数据库。这样,所加入的特征模板更加可靠,人脸识别质量显著提高。