Qualcomm Incorporated

Matthew Fischler

Michel Adib Sarkis

Ning Bi

Scott David Beith

Sairam Sundaresan

Ke-Li Cheng

Mithun Kumar Ranganath

Yingyong Qi

Stephane Pierre Villette

Suzana Arellano

Xuan Zou

Arthur Pajak

A device includes a memory and one or more processors configured to process sensor data to determine a semantical context associated with the sensor data. The one or more processors are also configured to generate adjusted face data based on the determined semantical context and face data. The adjusted face data includes an avatar facial expression that corresponds to the semantical context.

Avatar facial expressions based on semantical context

A method performed by an electronic device is described. The method includes receiving an image. The image depicts a face. The method also includes detecting at least one facial landmark of the face in the image. The method further includes receiving a depth image of the face and determining at least one landmark depth by mapping the at least one facial landmark to the depth image. The method also includes determining a plurality of scales of depth image pixels based on the at least one landmark depth and determining a scale smoothness measure for each of the plurality of scales of depth image pixels. The method additionally includes determining facial liveness based on at least two of the scale smoothness measures. Determining the facial liveness may be based on a depth-adaptive smoothness threshold and/or may be based on a natural face size criterion.

Systems and methods for facial liveness detection

Techniques and systems are provided for tracking objects in one or more images. For example, a trained network can be applied to a first image of a sequence of images to detect one or more objects in the first image. The trained network can be applied to less than all images of the sequence of images. A second image of the sequence of images and a detection result from application of the trained network to the first image are obtained. The detection result includes the detected one or more objects from the first image. A first object tracker can be applied to the second image using the detection result from application of the trained network to the first image. Applying the first object tracker can include adjusting one or more locations of one or more bounding boxes associated with the detected one or more objects in the second image to track the detected one or more objects in the second image. A second object tracker can also be applied to the second image to track at least one object of the detected one or more objects in the second image. The second object tracker is applied to more images of the sequence of images than the trained network and the first object tracker. Object tracking can be performed for the second image based on application of the first object tracker and the second object tracker.

Object tracking for neural network systems

Methods and apparatuses are disclosed for assisting a user in performing a three dimensional scan of an object. An example user device to assist with scanning may include a processor. The user device may further include a scanner coupled to the processor and configured to perform a three dimensional scan of an object. The user device may also include a display to display a graphical user interface, wherein the display is coupled to the processor. The user device may further include a memory coupled to the processor and the display, the memory including one or more instructions that when executed by the processor cause the graphical user interface to display a target marker for a three dimensional (3D) scan and display a scanner position marker to assist in moving the scanner to a preferred location and direction.

User interface to assist three dimensional scanning of objects

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San Diego, CA, United States of America

Matthew Fischler