The iris is a highly accurate biometric identifier. However widespread adoption is hindered by the difficulty of capturing high-quality iris images with minimal user cooperation. This paper describes a first-generation prototype iris identification system designed for stand-off cooperative access control. This system identifies individuals who stand in front of and face the system after 3.2 seconds on average. Subjects within a capture zone are imaged with a calibrated pair of wide-field-of-view surveillance cameras. A subject is located in three dimensions using face detection and triangulation. A zoomed near infrared iris camera on a pan-tilt platform is then targeted to the subject. The iris camera lens has its focal distance automatically adjusted based on the subject distance. Integrated with the iris camera on the pan-tilt platform is a near infrared illuminator that is composed of an array of directed LEDs. Video frames from the iris camera are processed to detect and segment the iris, generate a template and then identify the subject.
In some applications such as field stations, disaster situations or similar conditions, it is desirable to have a contactless, rugged means to collect fingerprint information. The approach described in this paper enables acceleration of the capture process by eliminating an otherwise system and finger cleanup procedure, minimizes the chance of the spread of disease or contaminations, and uses an innovative optical system able to provide rolled equivalent fingerprint information desirable for reliable 2D matching against existing databases. The approach described captures highresolution fingerprints and 3D information simultaneously using a single camera. Liquid crystal polarization rotators combined with birefringent elements provide the focus shift and a depth from focus algorithm extracts the 3D data. This imaging technique does not involve any moving parts, thus reducing cost and complexity of the system as well as increasing its robustness. Data collection is expected to take less than 100 milliseconds, capturing all four-finger images simultaneously to avoid sequencing errors. This paper describes the various options considered for contactless fingerprint capture, and why the particular approach was ultimately chosen.
We present a novel online inspection method for manufacturing processes that automatically adapts to variations in part and environmental properties. This method is based on a developmental learning architecture comprising a procedure that focuses attention to apparently defective regions, a recognition method that performs automatic feature derivation based on a set of training images and hierarchical classification, and an action step that controls attention and further decision processes. The method adapts to variations incrementally by updating rather than recreating the training information. Also, the method is capable of inspecting and training simultaneously. Addressing new inspection tasks requires neither re-programming and compatibility tests, nor quantitative knowledge about the image set, from a human developer. Instead, automatic or manual training of the inspection system according to simple guidelines is applied. These attributes allow the method to improve online performance with minimal ramp-up time. Our system performed inspection of three applications with low error rate and fast recognition, confirming its suitability for general-purpose, real-time, online inspection.
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