The present disclosure provides an apparatus and a method for fingerprint imaging. The apparatus includes: a light source configured to generate light, wherein a light intensity distribution of the light generated by the light source conforms to a preset encoding mode; a sensing surface on which the light generated by the light source forms a signal light with fingerprint information; and an imaging module configured to image the signal light to obtain a fingerprint image. Embodiments of the present disclosure can encode the fingerprint image by the encoding mode and can “encrypt” the fingerprint image by the encoding mode, thus a clear fingerprint image can be obtained by decoding based on obtaining the preset encoding mode, which can effectively improve the security of fingerprint imaging.

Some embodiments are directed to a biometric authentication system including headwear having a plurality of biosensors each configured to sample muscle activity so as to obtain a respective time-varying signal, a data store for storing a data set representing characteristic muscle activity for one or more users, and a processor configured to process the time-varying signals from the biosensors in dependence on the stored data set so as to determine a correspondence between a time-varying signal and characteristic muscle activity of one of the one or more users, and in dependence on the determined correspondence, authenticate the time-varying signals as being associated with that user.

The present disclosure is related to an optical encoder which is configured to provide precise coding reference data by feature recognition technology. To apply the present disclosure, it is not necessary to provide particular dense patterns on a working surface. The precise coding reference data can be generated by detecting surface features of the working surface.

The present disclosure is related to an optical encoder which is configured to provide precise coding reference data by feature recognition technology. To apply the present disclosure, it is not necessary to provide particular dense patterns on a working surface. The precise coding reference data can be generated by detecting surface features of the working surface.

Provided are a depth image processing method, and a small obstacle detection method and system. The method comprises calibration of sensors, distortion and epipolar rectification, data alignment, and sparse stereo matching. The depth image processing method and the small obstacle detection method and system of the present invention only requires execution of sparse stereo matching on hole portions of a structured light depth image, and do not requires stereo matching of the entire image, thereby significantly reducing the overall computation load for processing a depth image, and enhancing the robustness of a system.

In variants, a method for item recognition can include: optionally calibrating a sampling system, determining visual data using the sampling system, determining a point cloud, determining region masks based on the point cloud, generating a surface reconstruction for each item, generating image segments for each item based on the surface reconstruction, and determining a class identifier for each item using the respective image segments.

Potentially fraudulent documents can be identified automatically and flagged for further review. Document fraud detection can include a number of fraud detection tests, where if the document fails any of the tests, the document can be flagged for further review. Various tests can be performed that seek to determine whether a document has been altered. In one instance, a financial instrument can be scanned and analyzed to identify one or more layers associated with the financial instrument from a scanned representation, and a check of transaction details can be triggered when the financial instrument is associated with multiple layers.

An object detection system and method includes: an optical image sensor arranged to perform the following steps: capturing a calibration image during a calibration stage, dividing the calibration image into a plurality of quadrants, and calculating a parameter for each of the quadrants; capturing a plurality of raw images during a detection stage, dividing each image of the raw images into a plurality of quadrants, and calculating a parameter for each of the quadrants; comparing the respective parameters of each quadrant of a raw image with the respective parameters of each quadrant of the calibration image to generate a ratio value for each quadrant; and comparing the ratio value of each quadrant with a predetermined threshold. When each ratio value of specific quadrants of the quadrants is greater than the predetermined threshold, object detection is confirmed.

A polarized image acquisition section 11a acquires a polarized image of a target object having one or more polarization directions. A polarization parameter acquisition section 12-1 calculates the average brightness α of a polarization model on the basis of a non-polarized image subjected to sensitivity correction. Further, the polarization parameter acquisition section 12-1 calculates the amplitude β of the polarization model on the basis of the calculated average brightness α, pre-stored information regarding the zenith angle θ of the normal line of the target object, a refractive index r, and reflectance property information indicative of whether a subject is diffuse reflection or specular reflection. A polarization model detection section 13-1 is able to detect the polarization properties of the target object through the use of an image polarized in one or more polarization directions, by calculating the phase ϕ of the polarization model on the basis of a polarized image of the target object having one or more polarization directions, the average brightness α, and the amplitude β of the polarization model.

A polarized image acquisition section 11a acquires a polarized image of a target object having one or more polarization directions. A polarization parameter acquisition section 12-1 calculates the average brightness α of a polarization model on the basis of a non-polarized image subjected to sensitivity correction. Further, the polarization parameter acquisition section 12-1 calculates the amplitude β of the polarization model on the basis of the calculated average brightness α, pre-stored information regarding the zenith angle θ of the normal line of the target object, a refractive index r, and reflectance property information indicative of whether a subject is diffuse reflection or specular reflection. A polarization model detection section 13-1 is able to detect the polarization properties of the target object through the use of an image polarized in one or more polarization directions, by calculating the phase ϕ of the polarization model on the basis of a polarized image of the target object having one or more polarization directions, the average brightness α, and the amplitude β of the polarization model.