Fujitsu Corporation

Carnegie Mellon University

University of Tsukuba

Koichiro Niinuma

Laszlo A Jeni

Jeffrey F Cohn

Naoya Sogi

Kazuhiro Fukui

Tomoki Uchiyama

Heng Yu

In an example, a method may include obtaining, from a data source, first data including multiple frames each including a human face. The method may include automatically detecting, in each of the multiple frames, one or more facial landmarks and one or more action units (AUs) associated with the human face. The method may also include automatically generating one or more semantic masks based at least on the one or more facial landmarks, the one or more semantic masks individually corresponding to the human face. The method may further include obtaining a facial hyperspace using at least the first data, the one or more AUs, and the semantic masks. The method may also include generating a synthetic image of the human face using a first frame of the multiple frames and one or more AU intensities individually associated with the one or more AUs.

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A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including acquiring movie data including a plurality of consecutive frames calculating first likelihood of a class of the movie data by inputting the acquired movie data to a trained model, calculating an optical flow indicating movement of an area included in the movie data, based on the movie data generating occluded movie data by setting an occluded area in each of the frames included in the movie data, based on the optical flow, calculating second likelihood of a class of the occluded movie data by inputting the occluded movie data to the model identifying an area that affects identification of the class among areas in the movie data, based on the first likelihood and the second likelihood and displaying the identified area that affects identification of the class.

Non-transitory computer readable recording medium, information processing method and information processing apparatus

A method may include obtaining a facial image of a subject and identifying a number of new images to be synthesized with target AU combinations and categories of intensity. The method may also include synthesizing the number of new images using the facial image of the subject as the base image with the number of target AU combinations and categories of intensity with a number of new images that have different AU combinations than the facial image of the subject. The method may additionally include adding the number of new images to a dataset and training a machine learning system using the dataset to identify a facial expression of the subject.

Image synthesis for personalized facial expression classification

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Pittsburgh, PA, United States of America

Koichiro Niinuma