Qualcomm Incorporated

Peng Liu

Alberto Rico Alvarino

Lei Wang

Ning Bi

Zhen Wang

Michel Adib Sarkis

Ke-Li Cheng

Kuang-Man Huang

Vivek Padi

Kapil Bhattad

Gowrisankar Somichetty

Vijayvaradharaj Tirucherai Muralidharan

Shashidhar Vummintala

Systems and techniques are provided for facial expression recognition. In some examples, a system receives an image frame corresponding to a face of a person. The system also determines, based on a three-dimensional model of the face, landmark feature information associated with landmark features of the face. The system then inputs, to at least one layer of a neural network trained for facial expression recognition, the image frame and the landmark feature information. The system further determines, using the neural network, a facial expression associated with the face.

Facial expression recognition

Systems and techniques are provided for facial image augmentation. An example method can include obtaining a first image capturing a face. Using the first image, the method can determine, using a prediction model, a UV face position map including a two-dimensional (2D) representation of a three-dimensional (3D) structure of the face. The method can generate, based on the UV face position map, a 3D model of the face. The method can generate an extended 3D model of the face by extending the 3D model to include region(s) beyond a boundary of the 3D model. The region(s) can include a forehead region, a region surrounding at least a portion of the face, and/or other region. The method can generate, based on the extended 3D model, a second image depicting the face in a rotated position relative to a position of the face in the first image.

Image augmentation for analytics

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San Diego, CA, United States of America

Peng Liu