The face plays a key role in many real-world applications such as security systems, human computer interaction, remote monitoring of patients, video annotation, and gaming. Having detected the face, pattern recognition techniques and machine learning algorithms are applied to facial images, for example, to find the identity of a subject or analyze her/his emotional status. Though face and facial expression recognition in still images and in ideal imaging conditions have been around for many years, they have been less explored in video sequences in uncontrolled real-world videos. Given the ubiquitous presence of video cameras, face and facial expression recognition from such videos is becoming increasingly important for many applications, for instance for security surveillance, remote patient monitoring. Recognizing faces and facial expressions from real-world videos, however, remain challenging because of low video quality, illumination variation, head pose variation, and significant occlusion. Despite these challenges, video offers dynamics and motion information that is not available in still image and they can be exploited to improve the recognition. The purpose of this workshop is to bring together researchers who are working on developing face and facial expression recognition systems that involve non-ideal conditions, like those that might be present in a real-world video. We welcome research papers focusing on the following (and similar) topics:
- Video face recognition
- Video facial expression recognition
- Face and facial expression recognition from facial dynamics
- Face detection and tracking from video
- Multi-face clustering from video
- 3D face modeling from video
- Applications of video face recognition
- Applications of video facial expression recognition
The post-proceedings of FFER 2018 will be published by Springer’s LNCS series.
FFER’s predecessors proceedings: