A spatio-temporal descriptor for dynamic 3 D facial expression retrieval and recognition (original) (raw)

Real-time expression recognition from dynamic sequences of 3D facial scans

In this paper, we address the problem of person-independent facial expression recognition in dynamic sequences of 3D face scans. To this end, an original approach is proposed that relies on automatically extracting a set of 3D facial points, and modeling their mutual distances along time. Training an Hidden Markov Model for every prototypical facial expression to be recognized, and combining them to form a multi-class classifier, an average recognition rate of 76.3% on the angry, happy and surprise expressions of the BU-4DFE database has been obtained. Comparison with competitor approaches on the same database shows that our solution is able to obtain effective results with the clear advantage of an implementation that fits to real-time constraints.

A High-Resolution Spontaneous 3D Dynamic Facial Expression Database

2013

Abstract—Facial expression is central to human experience. Its efficient and valid measurement is a challenge that automated facial image analysis seeks to address. Most publically available databases are limited to 2D static images or video of posed facial behavior. Because posed and un-posed (aka “spontaneous”) facial expressions differ along several dimensions including complexity and timing, well-annotated video of un-posed facial behavior is needed.

A 3D Facial Expression Database For Facial Behavior Research

2006

Traditionally, human facial expressions have been studied using either 2D static images or 2D video sequences. The 2D-based analysis is incapable of handing large pose variations. Although 3D modeling techniques have been extensively used for 3D face recognition and 3D face animation, barely any research on 3D facial expression recognition using 3D range data has been reported. A primary factor for preventing such research is the lack of a publicly available 3D facial expression database. In this paper, we present a newly developed 3D facial expression database, which includes both prototypical 3D facial expression shapes and 2D facial textures of 2,500 models from 100 subjects. This is the first attempt at making a 3D facial expression database available for the research community, with the ultimate goal of fostering the research on affective computing and increasing the general understanding of facial behavior and the fine 3D structure inherent in human facial expressions. The new database can be a valuable resource for algorithm assessment, comparison and evaluation