Abstract: A system for remote identification of users. The system uses deep learning techniques for authenticating a user from an identification document and using automated verification of identification documents. Identification documents may be authenticated by validating security features. The system may determine features expected in a valid identification document and determine whether those features are present, employing techniques, such as determining whether direction-sensitive features are present. Liveness of a user indicated by the identification document may be determined with a deep learning model trained for identification of facial spoofing attacks.

Abstract: A system for remote identification of users. The system uses deep learning techniques for authenticating a user from an identification document, using automated verification of identification documents and detection that a live person identified by the document is present. Liveness of a user indicated by the identification document may be determined with a deep learning model trained for identification of facial spoofing attacks. The deep learning model may be trained using training data extracted from facial feature locations of training images.

Abstract: A system for remote identification of users. The system uses deep learning techniques for authenticating a user from an identification document and using automated verification of identification documents. Identification documents may be authenticated by validating security features. The system may determine features expected in a valid identification document and determine whether those features are present, employing techniques, such as determining whether direction-sensitive features are present. Liveness of a user indicated by the identification document may be determined with a deep learning model trained for identification of facial spoofing attacks.

Abstract: A system for remote identification of users. The system uses deep learning techniques for authenticating a user from an identification document, using automated verification of identification documents and detection that a live person identified by the document is present. Liveness of a user indicated by the identification document may be determined with a deep learning model trained for identification of facial spoofing attacks. The deep learning model may be trained using training data extracted from facial feature locations of training images.

Abstract: A camera head is provided that is adapted to interface with an existing host camera supported on an existing inspection vehicle of an analog-based pipeline inspection system. The camera head allows for capture, processing and storage of digital image data in a memory at the camera head. The camera head includes a digital-to-analog signal converter, and a control module causing selected images to be converted from digital format to analog format, and to be transmitted for navigation purposes via a conventional analog control cable to conventional analog video monitoring equipment in a conventional operator station. Digital Image and other data may be retrieved directly from the digital data memory of the camera head. This approach provides numerous advantages with respect to capture and review of images in digital format, while also permitting control of the vehicle for navigation purposes using an operator's existing analog-based monitoring and control equipment.