A front-end device is configured to capture a light point image of an authenticatee and send the light point image to the back-end device, where the light point image is an image captured from the authenticatee under irradiation of multi-light points, and the light point image includes a face of the authenticatee. The back-end device of the authentication system is configured to perform face anti-spoofing detection on the authenticatee based on the received light point image to obtain an authentication result.

Methods, systems, and computer programs are presented for object recognition performed by electronic devices. One method includes an operation for capturing three-dimensional (3D) images of a region over a surface using 3D cameras, the surface having a pattern and each 3D camera defining a respective camera coordinate system. For each camera, the 3D image is analyzed to identify a location of the pattern indicating an origin of a common coordinate system, and a coordinate transformation function is defined to convert data to the common coordinate system. Each 3D camera captures a 3D object image of an object on the surface that includes 3D object data. The 3D object data is transformed to the common coordinate system to obtain transformed 3D object data. The 3D object data is combined to obtain a composite 3D object data, and object recognition of the object is performed based on the composite 3D object data.

A three-dimensional (3D) image recognition system includes a first imaging sensor capable of collecting a first wavelength range of light and a second imaging sensor capable of collecting a second wavelength range of light. The first imaging sensor and the second imaging sensor are placed apart. The 3D image recognition system also includes a processor configured to identify at least one landmark area of a first image of an object collected by the first imaging sensor, and identify at least one matching landmark area in a second image of the object collected by the second imaging sensor. The processor is further configured to extract the 3D information of the object from the at least one landmark area of the images collected.

A system for capturing a 3D image of a subject includes a detection device which is structured to capture images of the subject and surrounding environment, a projection device which is structured to provide a source of structured light, and a processing unit in communication with the detection device and the projection device. The processing unit is programmed to: analyze an image of the subject captured by the detection device; modify one or more of: the output of the projection device or the intensity of a source of environmental lighting illuminating the subject based on the analysis of the image; and capture a 3D image of the subject with the detection device and the projection device using the modified one or more of the output of the projection device or the intensity of the source of environmental lighting illuminating the subject.

Additive manufacturing systems using artificial intelligence can identify an anomaly in a printed layer of an object from a generated topographical image of the printed layer. The additive manufacturing systems can also use artificial intelligence to determine a correlation between the identified anomaly and one or more print parameters, and adaptively adjust one or more print parameters. The additive manufacturing systems can also use artificial intelligence to optimize one or more printing parameters to achieve desired mechanical, optical and/or electrical properties.

A depth calculation processor, a data processing method, and a 3D image device are disclosed herein. The depth calculation processor includes: two input ports to receive a first image data, wherein the first image data comprises structured light image acquired under projection of structured light; an input switch connected to the input ports and to convey all or some of the first image data from the input ports; a data processing engine connected to the input switch and to process the first image data that is output through the input switch and to output a second image data, wherein the second image data comprises a depth map, wherein the data processing engine comprises a depth processing engine to process the structured light image to obtain the depth map; and one output port connected to the data processing engine and to output the second image data to a main device.

A gesture operation method based on depth values and the system thereof are revealed. A stereoscopic-image camera module acquires a first stereoscopic image. Then an algorithm is performed to judge if the first stereoscopic image includes a triggering gesture. Then the stereoscopic-image camera module acquires a second stereoscopic image. Another algorithm is performed to judge if the second stereoscopic image includes a command gesture for performing the corresponding operation of the command gesture.

Apparatus, methods, and other embodiments associated with a key duplication machine are described. In one embodiment, an assembly for duplicating a master key includes an optical imaging device, a logic, a clamping assembly, and a cutting member. The optical imaging device is capable of capturing an optical image of at least a portion of the master key. The logic is capable of determining a key pattern of the master key from the optical image of the master key. The clamping assembly is capable of clamping a key blank and the cutting member is capable of cutting a key pattern into said key blank.

A wound assessment method which can estimate a moisture level of the wound, and related image capture device. The wound area is imaged at least twice where the wound is illuminated under different illumination light intensities. The first image captured using a relatively low illumination light intensity is analyzed to assess the wound, for example measuring its size, color and texture. The second image captures using a relatively high illumination light intensity (e.g. using a flash) is analyzed to estimate the moisture level of the wound. The moisture level estimation method extracts white connected components from the second image, and estimates the moisture level based on the number, sizes, and centroid distribution of the white connected components. A 3D image of the wound may also be captured, e.g. using a structured-light 3D scanner of the image capture device.

The present document describes methods and systems for the automatic inspection of material quality. A set of lights with a geometric pattern is cast on a material to be analyzed. Depending on the material being inspected, same may act as a mirror and the reflected image is captured by a capture device, or the light passes through the material being inspected and the image is captured by a capture device. Defects in the material can be detected by the distortion caused by same in the pattern of the reflected image or passing through. Finally, software is used to identify and locate these distortions, and consequently the defects in the material. This classification of defects is carried out using artificial intelligence techniques.