Automatic facial expression analysis: a survey

Fasel, Beat; Luettin, Juergen

doi:10.1016/s0031-3203(02)00052-3

Cited by 1,532 publications

(734 citation statements)

References 53 publications

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“…Spatial information comes from facial features in a single video frame. On the other hand, temporal information stands for the relationship between facial features revealed in consecutive video frames [2], [9], [22]. CNNs are well known for their great ability in learning abstract spatial features from a given image (single frame) [11].…”

Section: A Methodsmentioning

confidence: 99%

Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification

Haamer

Kulkarni

Imanpour

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Abstract-Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames.

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Section: A Methodsmentioning

confidence: 99%

Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification

Haamer

Kulkarni

Imanpour

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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“…For an overview of the current techniques see [26]. Facial expression dynamics, have also been used to assist the identification of individuals [4,9], where it was proposed that there are individual differences in how expressions are formed, it is suggested that this information could be used to augment facial recognition systems.…”

Section: Computer Vision Approachesmentioning

confidence: 99%

Investigating the Dynamics of Facial Expression

Reilly

Ghent

McDonald

2006

Advances in Visual Computing

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Abstract. This paper is concerned with capturing the dynamics of facial expression. The dynamics of facial expression can be described as the intensity and timing of a facial expression and its formation. To achieve this we developed a technique that can accurately classify and differentiate between subtle and similar expressions, involving the lower face. This is achieved by using Local Linear Embedding (LLE) to reduce the dimensionality of the dataset and applying Support Vector Machines (SVMs) to classify expressions. We then extended this technique to estimate the dynamics of facial expression formation in terms of intensity and timing.

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“…Although much progress has been made since then, accurate recognition of facial expressions is still a challenging problem due to the subtlety, complexity and variability of facial expressions [5,6].…”

Section: Introductionmentioning

confidence: 99%

A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences

Fan

Tjahjadi

2015

Pattern Recognition

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Original citation:Fan, Xijian and Tjahjadi, Tardi. (2015) A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences. Pattern Recognition, 48 (11 Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. AbstractFacial expression causes different parts of the facial region to change over time and thus dynamic descriptors are inherently more suitable than static descriptors for recognising facial expressions. In this paper, we extend the spatial pyramid histogram of gradients to spatio-temporal domain to give 3-dimensional facial features and integrate them with dense optical flow to give a spatio-temporal descriptor which extracts both the spatial and dynamic motion information of facial expressions. A multi-class support vector machine based classifier with one-to-one strategy is used to recognise facial expressions. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme demonstrate that the integrated framework achieves a better performance than using individual descriptor separately. Compared with six state of the art methods, the proposed framework demonstrates a superior performance.

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Automatic facial expression analysis: a survey

Cited by 1,532 publications

References 53 publications

Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification

Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification

Investigating the Dynamics of Facial Expression

A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences

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