Abstract: The present disclosure is directed to face detection window refinement using depth. Existing face detection systems may perform face detection by analyzing portions of visual data such as an image, video, etc. identified by sub-windows. These sub-windows are now determined only based on pixels, and thus may number in the millions. Consistent with the present disclosure, at least depth data may be utilized to refine the size and appropriateness of sub-windows that identify portions of the visual data to analyze during face detection, which may substantially reduce the number of sub-windows to be analyzed, the total data processing burden, etc. For example, at least one device may comprise user interface circuitry including capture circuitry to capture both visual data and depth data. Face detection circuitry in the at least one device may refine face detection by determining criteria for configuring the sub-windows that will be used in face detection.

Abstract: Movement of a user in a user space is mapped to a first portion of a command action stream sent to a telerobot in a telerobot space. An immersive feedback stream is provided by the telerobot to the user. Upon movement of user into or proximate to a margin of user space, the first portion of the command action stream may be suspended. The user may re-orient in the user space, and may then continue to move, with movement mapping re-engaged and resumption of transmission of a second portion of command action stream. In this way, user may control a telerobot via movement mapping, even though user space and telerobot space may not be the same size.

Abstract: Various systems and methods for implementing a facial gesture-based CAPTCHA are described herein. A system for securing a resource comprises a presentation module to present on a display of the system, a facial gesture challenge prompt to a user; an image processor to: obtain an image from a camera coupled to the compute device, the image including a facial expression of the user; and obtain analysis of the image, the analysis to determine whether the facial expression matches the facial gesture challenge prompt; and wherein the presentation module is to provide an indication to the user of whether the facial expression matches the facial gesture challenge prompt.

Abstract: The present disclosure is directed to face detection window refinement using depth. Existing face detection systems may perform face detection by analyzing portions of visual data such as an image, video, etc. identified by sub-windows. These sub-windows are now determined only based on pixels, and thus may number in the millions. Consistent with the present disclosure, at least depth data may be utilized to refine the size and appropriateness of sub-windows that identify portions of the visual data to analyze during face detection, which may substantially reduce the number of sub-windows to be analyzed, the total data processing burden, etc. For example, at least one device may comprise user interface circuitry including capture circuitry to capture both visual data and depth data. Face detection circuitry in the at least one device may refine face detection by determining criteria for configuring the sub-windows that will be used in face detection.

Abstract: Movement of a user in a user space is mapped to a first portion of a command action stream sent to a telerobot in a telerobot space. An immersive feedback stream is provided by the telerobot to the user. Upon movement of user into or proximate to a margin of user space, the first portion of the command action stream may be suspended. The user may re-orient in the user space, and may then continue to move, with movement mapping re-engaged and resumption of transmission of a second portion of command action stream. In this way, user may control a telerobot via movement mapping, even though user space and telerobot space may not be the same size.

Abstract: At times, subjects having one or more flawed features, such as eyes closed or a frown, may appear in images acquired using an image acquisition device. An image editing circuit removes such flawed features by identifying the flawed features in the first image and uniquely identifying the subject having the flowed features. The image editing circuit autonomously identifies historical images in which the subject appears and in which the subject has an unflawed feature corresponding to the flawed feature in the first image. The image editing circuit autonomously extracts the unflawed feature from the historical image and replaces the flawed feature in the first image with the unflawed feature.