Automatic Analysis of Facial Expressions: The State of the Art

By Maja Pantic, Léon J. M. Rothkrantz. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

2,049 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/087a6f471b5177014b08a342968901e1ef083ed1

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Citations in the corpus

Facial expression recognition based on Local Binary Patterns: A comprehensive study (2009, background): “Much progress has been made in the last decade, and a thorough survey of the exiting work can be found in [ 1 ,2].”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, background): “tial surveys of these early works [214], [215], [216].”
Authentic facial expression analysis (2004, background): “The two recent surveys in the area [12, 22] provide an in depth review of many of the research done in recent years.”
Gesture Recognition: A Survey (2007, background): “There are two major approaches to automated face recognition [43].”
Facial expression recognition from video sequences: temporal and static modeling (2003, background): “Darwin [7] held an ethological view of emotional expressions , arguing that the expressions from infancy and lower life forms exist in adult humans.”
Active and dynamic information fusion for facial expression understanding from image sequences (2005, background): “A thorough survey of the existing works can be found in [12], [13], [14].”
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database (2014, neutral): “, anger, disgust, fear, happiness, sadness, and surprise) [3,5,13].”
A survey on face detection in the wild: Past, present and future (2015, background): “Automatic face detection is the cornerstone of all applications revolving around automatic facial image analysis including, but not limited to, face recognition and verification [1], face tracking for surveillance [2], facial behaviour analysis [3], facial attribute recognition [4] (i.”
A dependence maximization view of clustering (2007, background): “…a Gaussian kernel exp(-sI x - x ' I2) with s = d-1 (d:dimension of the data) for the kernel matrix K. Hierarchical Facial Expression Clustering Computer recognition of facial expressions is an important component in automated understanding of human communication (Pantic & Rothkrantz, 2000).”
Facial Expression Analysis under Partial Occlusion (2018, methodology): “In earlier surveys on FEA [Pantic and Rothkrantz 2000], [Fasel and Luettin 2003], no study reported specifically designed techniques to overcome facial occlusion.”
Robust Facial Expression Recognition Based on Local Directional Pattern (2010, background): “Even though much work has already been done on automatic facial expression recognition [6], [7], higher accuracy with reasonable speed still remains a great challenge [8].”
Face recognition from a single image per person: A survey (2006, background): “All rights reserved. doi:10.1016/j.patcog.2006.03.013 [6], Pantic and Rothkrantz [7] surveyed the automatic facial expression analysis, Daugman [3] pointed out several critical issues involved in an effective face recognition system, while the most recent and comprehensive survey is possibly from th”
Emotion Recognition through Multiple Modalities: Face, Body Gesture, Speech (2008, methodology): “Facial expressions [12] [13], vocal features [14] [15], body movements and postures [16] [17] [18], physiological signals [19] have been used as inputs during these attempts, while multimodal emotion recognition is currently gaining ground [20] [21] [22].”
Facial Landmark Detection: A Literature Survey (2018, background): “For example, facial expression recognition [68] and head pose estimation algorithms [66] may heavily rely on the facial shape information provided by the landmark locations.”
Comparing different classifiers for automatic age estimation (2004, background): “In this context, systems capable of identifying faces [14], recognizing emotions [19], gender [17], and head orientation [16] have been developed.”