• VeriFinger SDK

 

VERIFINGER SDK
FINGERPRINT IDENTIFICATION FOR STAND-ALONE OR WEB SOLUTIONS
VeriFinger is a fingerprint identification technology designed for biometric systems developers and integrators. The technology assures system performance with fast, reliable fingerprint matching in 1-to-1 and 1-to-many modes.
Available as a software development kit that allows development of stand-alone and Web-based solutions on Microsoft Windows, Linux, Mac OS X, iOS and Android platforms.

VERIFINGER SDK
用于单机系统或WEB解决方案的指纹识别
VeriFinger是生物识别系统开发人员和集成商适用的指纹识别技术。该技术在1:1和1:N模式下,均可快速、可靠的指纹匹配,保证系统的高性能.
作为软件开发工具包,它允许在Microsoft Windows、Linux上开发独立和基于Web的解决方案,兼容Mac OS X、iOS和Android平台。

 

FEATURES AND CAPABILITIES

  • 1500+ end-user product brands in 100+ countries used the VeriFinger algorithm over the past 21 years.
  • Full NIST MINEX compliance, FpVTE and FVC awards since 2000.
  • Rolled and flat fingerprint matching that is tolerant to fingerprint translation, rotation and deformation.
  • Compact fingerprint template and unlimited database size.
  • Available as multiplatform SDK that supports multiple scanners and multiple programming languages.
  • Reasonable prices, flexible licensing and free customer support.

产品特点和功能

  • 在过去21年中,100多个国家1500+终端用户产品品牌使用了VeriFinger算法。
  • 完全遵守NIST MINEX2000年以来每年均获FpVTEFVC荣誉
  • 兼容滚动和按捺的指纹匹配,容忍指纹的平移、旋转和变形。
  • 紧凑的指纹模板和无限制的数据库大小。
  • 支持多种扫描器和多种编程语言的多平台SDK
  • 合理的价格,灵活的许可和免费的客户支持。

In 1998 Neurotechnology developed VeriFinger, a fingerprint identification technology designed for biometric system integrators. Since that time, Neurotechnology has released more than 10 major and minor versions of the VeriFinger, providing most powerful fingerprint recognition algorithms to date. Numerous awards in competitions and technology evaluations, including FVC and FpVTE, have been received by VeriFinger.
The VeriFinger algorithm is based on deep neural networks and follows the commonly accepted fingerprint identification scheme, which uses a set of specific fingerprint points (minutiae) along with a number of proprietary algorithmic solutions that enhance system performance and reliability. Some are listed below:

  • Rolled and flat fingerprints matching. The VeriFinger algorithm matches flat-to-rolled, flat-to-flat or rolled-to-rolled fingerprints with a high degree of reliability and accuracy, as it is tolerant to fingerprint deformations. Rolled fingerprints have much bigger deformation due to the specific scanning technique (rolling from nail to nail) than those scanned using the "flat" technique. Conventional "flat" fingerprint identification algorithms usually perform matching between flat and rolled fingerprints less reliably due to the mentioned deformations of rolled fingerprints.
  • Tolerance to fingerprint translation, rotation and deformation. VeriFinger's proprietary fingerprint template matching algorithm is able to identify fingerprints even if they are rotated, translated, deformed and have only 5 - 7 similar minutiae (usually fingerprints of the same finger have 20 - 40 similar minutiae) and matches up to 40,000 flat fingerprints per second (see technical specifications for more details).
  • Identification capability. VeriFinger functions can be used in 1-to-1 matching (verification), as well as 1-to-many mode (identification).
  • Image quality determination. VeriFinger is able to ensure that only the best quality fingerprint template will be stored into database by using fingerprint image quality determination during enrollment.
  • Adaptive image filtration. This algorithm eliminates noises, ridge ruptures and stuck ridges for reliable minutiae extraction – even from poor quality fingerprints – with a processing time of 0.6 seconds.
  • Features generalization mode. This fingerprint enrollment mode generates the collection of generalized fingerprint features from a set of fingerprints of the same finger. Each fingerprint image is processed and features are extracted. Then the features collection set is analyzed and combined into a single generalized features collection, which is written to the database. This way, the enrolled features are more reliable and the fingerprint recognition quality considerably increases.
  • Compact fingerprint template. VeriFinger allows to configure the number and size of fingerprint features in a fingerprint template. Combined with unlimited database size, this capability allows to optimize target system size and performance.
  • Scanner-specific algorithm optimizations. VeriFinger 11.0 includes algorithm modes that help to achieve better results for the supported fingerprint scanners.

1998年,神网科技有限公司开发了VeriFinger指纹识别技术,专为生物识别系统集成商服务。从那时起,我们已经发布了10多个主要和次要版本的VeriFinger,提供了迄今为止最强大的指纹识别算法。在比赛和技术评估方面获奖无数,包括FVC和FpVTE。
VeriFinger算法基于深度神经网络,采用了常用的指纹识别方案,使用特定的指纹点(细节点)和一些专有的算法解决方案,提高了系统的性能和可靠性。特点如下:

  • 兼容滚动和平捺指纹匹配。VeriFinger算法具有很高的可靠性和准确性,因为它可以容忍指纹的变形。滚动指纹由于特定的扫描技术(从指甲的一侧滚动到另一侧)的变形要比使用平捺技术的指纹要大得多。传统的平捺指纹识别算法会由于滚动指纹的变形,通常对平捺指纹和滚动指纹之间的匹配不那么可靠。
  • 对指纹平移、旋转和变形的容忍度。VeriFinger的专有指纹模板匹配算法能够识别指纹,即使它们被旋转、转换、变形,并且只有5-7个相似的细节(通常同一手指的指纹有20-40相似的细节),并且匹配多达每秒4万枚平捺指纹。
  • 识别能力VeriFinger函数可以用于1对1匹配(验证)以及一对多模式(识别)
  • 图像质量测定VeriFinger能够保证在注册过程中,通过指纹图像质量的确定,只将质量最好的指纹模板存储到数据库中。
  • 自适应图像过滤 该算法消除了噪声,脊破裂和粘脊,提供可靠的细节提取——即使是从劣质指纹中提取——处理时间为0.6秒。
  • 特征归一化模式 该指纹注册模式从同一手指的一组指纹中生成广义指纹特征集合。对每个指纹图像进行处理,提取特征。然后对特征集进行分析,并将其组合成一个单一的广义特征集合,并将其写入数据库。这样,登记的特征更加可靠,指纹识别质量大大提高。
  • 紧凑的指纹模板。VeriFinger允许在指纹模板中配置指纹特征的数量和大小。结合无限数据库大小,此功能允许优化目标系统的大小和性能。
  • 特定于扫描仪的算法优化。VeriFinger 11.0包含为许多推荐指纹扫描器的图像特别优化参数。