VeriEye iris identification technology is designed to include many proprietary solutions that enable robust eye iris enrollment under various conditions and fast iris matching in 1-to-1 and 1-to-many modes. Engineered with capabilities to implement advanced iris segmentation, enrollment and matching including
Robust eye iris detection even when there are obstructions to the image, visual noise and/or different levels of illumination.
Gazing-away eyes are correctly detected, segmented and t...ransformed as if looking directly into the camera.
Automatic interlacing detection and correction results in maximum quality of iris features templates from moving iris images.
Eyelids, which partially occlude an iris, are detected and segmented from the iris image.
VeriEye is 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.
VeriEye Standard SDK is designed for PC-based, embedded and mobile biometric application development. It includes Iris Matcher and Extractor component licenses, programming samples and tutorials, iris scanner support modules and software documentation
SentiSight is designed to be as universal as possible and is able to perform fully automatic and manual object learning. SentiSight has two operation modes: learning and recognition. In learning mode, the SentiSight algorithm creates an object model by extracting object features from an image or video. In recognition mode, SentiSight finds and tracks objects with features matching those previously stored in object models.
SentiSight is designed to be as universal as possible and is able to p...erform fully automatic or manual object learning. The technology can be used for a wide range of tasks, including:
Some of the potential applications for SentiSight include security systems, vision systems for robots, machine vision (like parts recognition in production lines), search engines that recognize objects in picture files, road signs recognition, etc.
Recognition of documents, stamps, labels, packaging and other items for sorting, logo masking, usage monitoring and similar applications. Object counting and inspection for assembly lines and other industrial applications
Augmented and extended reality applications for toys, games, devices and Web applications such as: smart toys for children that recognize cards, images, pictograms, etc.; recognition of places based on photographs; recognition of products such as beverages, foods and other consumer goods.
Robotic vision for navigation and manipulation, Law enforcement applications for identification, such as tattoo recognition.
SentiSight Embedded is designed for developers who want to use computer vision-based object recognition in their applications for smartphones, tablets and other mobile devices. Through manual or fully automatic object learning it enables searching for learned objects in images or videos from built-in cameras with PC-like accuracy.
SentiSight Embedded is available as a software development kit that provides for the development of object recognition applications for the devices that are runnin...g Android OS with an innovative algorithm, that is tolerant to appearance, object scale, rotation and pose.
PC-level accuracy of object detection and processing with mobile devices.
Smatphone built-in cameras are suitable for obtaining object images.
Compatability and interoperability with PC-side SentiSight-based products.
Reasonable prices, flexible licensing and free customer support.
OBJECT LEARNING AND RECOGNITION PROCESSES
SentiSight Embedded has two operation modes: Learning and Recognition. In learning mode, the SentiSight algorithm creates an object model by extracting object features from an image or video. In recognition mode, SentiSight finds and tracks objects with features matching those previously stored in object models.
Object Learning Process
In order to recognize an object in an image, the appearance of an object must fi rst be catalogued. In the learning phase, SentiSight algorithms extract specifi c object features from a video stream or single image and save them into an object’s model.
In many cases there is information in a video or single image beyond the features of an object you wish SentiSight to learn such as a background, other objects in the room, a hand holding the target object. For SentiSight to properly learn an object, information about the exact location of the object within the image should be provided.
SentiSight supports 2 methods of object learning:
Manual and Automatic.
Manual object learning
This is suitable for most situations. A user performs the following steps for manual object learning in the SentiSight-based application:
Outline an object’s shape on an image by marking the object’s corner points to build a polygon. The image can be provided from an image file, video file or live video stream.
Select the algorithm to use: blob-based, shape-based, or both.
As an option, additional images of the object may be provided, repeating Step 1 for each image. The algorithm assists the user by estimating an approximate shape for the object if the image is recognized by way of previously catalogued images. Learning the object from different sides and angles results in better recognition quality.
Input the learned object name (ID) into the system. Automatic object learning is suitable for lightweight, movable objects. This learning procedure is based on detecting an object through the exclusion of a static background and the object’s holder (usually a hand). A fixed camera is highly recommended for this process.
Automatic object learning
A user must perform these steps for automatic object learning in the SentiSight-based application:
Select a background and position the camera.
Select a holder (an object that will be used to hold and move the object to be learned). A user’s hand can be the “holder”.
The “holder,” if it is not a rigid onject, should be presented to the camera in various poses and confi gurations so SentiSight can learn it.
Select the algorithm to use: blob-based, shape-based, or both.
After the holder has been learned, SentiSight is ready to learn the object itself. Use the holder to rotate and move the object, both closer to, and further from, the camera.
Input the learned object name (ID) into the system.
The automatic method requires the use of live video or separate video / image sets of background, holder andobject. Other background elements may be learned together with the object if the object is hardly separable from the background. Too little disparity between object and background, if not learned together, may affect the ability of the algorithm to recognize an object’s unique qualities, possibly resulting in the object being misclassifi ed along with other objects having the same background.
Manual object learning should be used for objects that cannot be moved or if there is no way to provide separate media of an object’s background and/or holder. Automatic learning requires less user interaction with the system, but it is not as precise as manual learning. Manual learning is suitable, generally, for a wider range of cases.