Abstract: Methods, systems, and apparatuses are provided to automatically determine whether an image is spoofed. For example, a computing device may obtain an image, and may execute a trained convolutional neural network to ingest elements of the image. Further, and based on the ingested elements of the image, the executed trained convolutional neural network generates an output map that includes a plurality of intensity values. In some examples, the trained convolutional neural network includes a plurality of down sampling layers, a plurality of up sampling layers, and a plurality of joint spatial and channel attention layers. Further, the computing device may determine whether the image is spoofed based on the plurality of intensity values. The computing device may also generate output data based on the determination of whether the image is spoofed, and may store the output data within a data repository.

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media to implement a heterogenous biometric authentication process in a control system. For example, a method may include detecting the presence of a first person at a first time period and in an area associated with a function controlled by a control system. The method may include transmitting an authentication request to a first device detected by the control system, and receiving an authentication response from the first device. The authentication response includes information related to a biometric authentication performed at the first device. The method may further include authenticating the first person in the control system based on the information related to the biometric authentication. The method may then perform the function based on the authentication.

Abstract: Methods, systems, and apparatuses are provided to automatically determine whether an image is spoofed. For example, a computing device may obtain an image, and may execute a trained convolutional neural network to ingest elements of the image. Further, and based on the ingested elements of the image, the executed trained convolutional neural network generates an output map that includes a plurality of intensity values. In some examples, the trained convolutional neural network includes a plurality of down sampling layers, a plurality of up sampling layers, and a plurality of joint spatial and channel attention layers. Further, the computing device may determine whether the image is spoofed based on the plurality of intensity values. The computing device may also generate output data based on the determination of whether the image is spoofed, and may store the output data within a data repository.

Abstract: Systems and techniques are provided for performing video-based activity recognition. For example, a process can include extracting, using a first machine learning model, first one or more features from a first frame and second one or more features from a second frame. The first one or more features and the second one or more features are associated with a person driving a vehicle. The process can include processing, using a second machine learning model, the first one or more features and the second one or more features. The process can include determining, based on processing of the first one or more features and the second one or more features using the second machine learning model, at least one activity associated with the person driving the vehicle.

Abstract: Systems and techniques are provided for performing video-based activity recognition. For example, a process can include extracting, using a first machine learning model, first one or more features from a first frame and second one or more features from a second frame. The first one or more features and the second one or more features are associated with a person driving a vehicle. The process can include processing, using a second machine learning model, the first one or more features and the second one or more features. The process can include determining, based on processing of the first one or more features and the second one or more features using the second machine learning model, at least one activity associated with the person driving the vehicle.

Abstract: Methods, systems, and devices for object detection are described. A device may receive an image, and detect, via a first stage of a cascade neural network, object recognition information over one or more angular orientations during a first pass. The device may determine, via a second stage of the cascade neural network, a confidence score associated with one or more of the candidate object in the image, the candidate bounding box associated with the candidate object in the image, or one or more object features of the candidate object in the image, or an orientation of the candidate object in the image, or a combination thereof. The device may identify, via a third stage of the cascade neural network, whether to detect the object recognition information during a second pass based on the confidence score satisfying a threshold.

Abstract: Methods, systems, and devices for image processing are described. A device may receive a first frame including a candidate object. The device may detect first object recognition information based on the first frame or a portion of the first frame. The first object recognition information may include the candidate object or a first candidate bounding box associated with the candidate object. The device may detect second object recognition information based on the first object recognition information, a second frame, or a portion of the second frame. The second object recognition information may include the candidate object in the second frame, a second candidate bounding box associated with the candidate object, or features of the candidate object. The device may estimate motion information associated with the candidate object in the first frame, and track the candidate object in the second frame based on the motion information.

Abstract: Methods, systems, and devices for object detection are described. A device may receive an image, and detect, via a first stage of a cascade neural network, object recognition information over one or more angular orientations during a first pass. The device may determine, via a second stage of the cascade neural network, a confidence score associated with one or more of the candidate object in the image, the candidate bounding box associated with the candidate object in the image, or one or more object features of the candidate object in the image, or an orientation of the candidate object in the image, or a combination thereof. The device may identify, via a third stage of the cascade neural network, whether to detect the object recognition information during a second pass based on the confidence score satisfying a threshold.

Abstract: The present invention relates a structure and method of manufacturing a unitary double-blade wind shield wiper. The method comprises a thermal forming step, a cutting step, a folding step and preferably a bonding step. In the thermal forming step, a rubber plate is imprinted into a rubber product which contains a plurality of continually connected wind shield wipers with a plurality of folding guide grooves. In the cutting step, the rubber product is divided into a plurality of independent wind shield wipers, wherein each of the independent wind shield wipers has a pair of blades extending in opposite directions to each other. In the folding step, each of the independent wind shield wipers is folded along the folding guide groove thereby to make the pair of blades parallel to each other. Preferable, further to impose the bonding step to fix the independent wind shield wipers into a permanent configuration, so as to be a more durable unitary double-blade wind shield wiper.