Method, an electronic device and a storage medium for living body detection based on face recognition are disclosed. The method comprises: obtaining to-be-detected infrared image and visible light image; performing edge detection and texture feature extraction on the infrared image, and feature extraction on the visible light image through a convolutional neural network; and determining whether the infrared and visible light images pass living body detection based on results of the edge detection and texture feature extraction on the to-be-detected infrared image, and a result of feature extraction on the to-be-detected visible light image through the convolutional neural network. The method, an electronic device and a storage medium for living body detection based on face recognition combine the advantages of three technologies of edge detection, texture feature extraction and convolution neural network, effectively perform living body detection, and improve the determination accuracy.

Method, an electronic device and a storage medium for living body detection based on face recognition are disclosed. The method comprises: obtaining to-be-detected infrared image and visible light image; performing edge detection and texture feature extraction on the infrared image, and feature extraction on the visible light image through a convolutional neural network; and determining whether the infrared and visible light images pass living body detection based on results of the edge detection and texture feature extraction on the to-be-detected infrared image, and a result of feature extraction on the to-be-detected visible light image through the convolutional neural network. The method, an electronic device and a storage medium for living body detection based on face recognition combine the advantages of three technologies of edge detection, texture feature extraction and convolution neural network, effectively perform living body detection, and improve the determination accuracy.

Various techniques are provided for increasing contrast of gas features in a scene. In one example, a method includes receiving a captured infrared image comprising a gas feature and a scene feature. The captured infrared image comprises a first range of pixel values associated with a first temperature range of the gas feature and the scene feature. The method also includes applying a spatial filter to the captured infrared image to provide a spatially filtered infrared image retaining the gas feature and removing the scene feature. The spatially filtered infrared image comprises a second range of pixel values associated with a second temperature range of the gas feature without the additional scene feature to exhibit increased gas contrast over the captured infrared image. Additional methods and systems are also provided.

Various techniques are provided for increasing contrast of gas features in a scene. In one example, a method includes receiving a captured infrared image comprising a gas feature and a scene feature. The captured infrared image comprises a first range of pixel values associated with a first temperature range of the gas feature and the scene feature. The method also includes applying a spatial filter to the captured infrared image to provide a spatially filtered infrared image retaining the gas feature and removing the scene feature. The spatially filtered infrared image comprises a second range of pixel values associated with a second temperature range of the gas feature without the additional scene feature to exhibit increased gas contrast over the captured infrared image. Additional methods and systems are also provided.

A method and system is disclosed for analyzing image data of a subject. The image data can be collected with an imaging system in a selected manner and/or motion. The image data may include selected overlap and be acquired with an imaging system that generates a plurality of perspectives for more than one location. An automatic system and method may then define or identify various features and/or allow for registration for alternative image data.

A method and system is disclosed for analyzing image data of a subject. The image data can be collected with an imaging system in a selected manner and/or motion. The image data may include selected overlap and be acquired with an imaging system that generates a plurality of perspectives for more than one location. An automatic system and method may then define or identify various features and/or allow for registration for alternative image data.

A method and system is disclosed for analyzing image data of a subject. The image data can be collected with an imaging system in a selected manner and/or motion. The image data may include selected overlap and be acquired with an imaging system that generates a plurality of perspectives for more than one location. An automatic system and method may then define or identify various features and/or allow for registration for alternative image data.

An artificial neural network-based method for selecting a surface type of an object is suitable for selecting a plurality of objects. The artificial neural network-based method for selecting a surface type of an object includes performing surface type identification on a plurality of object images by using a plurality of predictive models to obtain a prediction defect rate of each of the predictive models, wherein the object images correspond to surface types of a part of the objects, and cascading the predictive models according to the respective prediction defect rates of the predictive models into an artificial neural network so as to select the remaining objects.

In general, devices, systems, and methods for fiducial identification and tracking are provided.

In general, devices, systems, and methods for fiducial identification and tracking are provided.