Facial Expression Analysis under Partial Occlusion

Zhang, Ligang; Verma, Brijesh; Tjondronegoro, Dian; Chandran, Vinod

doi:10.1145/3158369

Cited by 919 publications

(72 citation statements)

References 141 publications

(254 reference statements)

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“…The work in [29] is about face detection and recognition, and facial expression analysis based on Local Binary Patterns (LBP). Zhang et al [30] concentrate on the problem of partial occlusion in FER and outline existing challenges and possible opportunities. Unlike most studies based on existing images or videos, Deshmukh et al [31] summarise the latest advances in the algorithms and techniques used in distinct phases of real-time FER.…”

Section: Differences With Existing Survey and Contributionsmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches. Methodologically, to highlight the differences and similarities, we propose a general framework of a conventional FER approach and review the possible technologies that can be employed in each component. As for deep learning-based methods, four kinds of neural network-based state-of-the-art FER approaches are presented and analysed. Besides, we introduce seventeen commonly used FER datasets and summarise four FER-related elements of datasets that may influence the choosing and processing of FER approaches. Evaluation methods and metrics are given in the later part to show how to assess FER algorithms, along with subsequent performance comparisons of different FER approaches on the benchmark datasets. At the end of the survey, we present some challenges and opportunities that need to be addressed in future.

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Section: Differences With Existing Survey and Contributionsmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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“…Emotion recognition in speech (ERS), is a well-studied field in the domain of affective computing [1]. Using the speech signal as a modality has some advantages compared to the visual modality, e. g., that there are no occlusions [2,3]. Nevertheless, the best performances are usually obtained when following a multi-modal approach fusing the acoustic, linguistic, and visual domains [4].…”

Section: Introductionmentioning

confidence: 99%

Continuous Emotion Recognition in Speech — Do We Need Recurrence?

Schmitt¹,

Cummins²,

Schuller³

2019

Interspeech 2019

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Emotion recognition in speech is a meaningful task in affective computing and human-computer interaction. As human emotion is a frequently changing state, it is usually represented as a densely sampled time series of emotional dimensions, typically arousal and valence. For this, recurrent neural network (RNN) architectures are employed by default when it comes to modelling the contours with deep learning approaches. However, the amount of temporal context required is questionable, and it has not yet been clarified whether the consideration of long-term dependencies is actually beneficial. In this contribution, we demonstrate that RNNs are not necessary to accomplish the task of time-continuous emotion recognition. Indeed, results gained indicate that deep neural networks incorporating less complex convolutional layers can provide more accurate models. We highlight the pros and cons of recurrent and nonrecurrent approaches and evaluate our methods on the public SEWA database, which was used as a benchmark in the 2017 and 2018 editions of the AudioVisual Emotion Challenge.

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“…Overall, webcams, tripod-mounted cameras, and PTZ cameras do not provide an optimal solution for recording facial expressions. They commonly suffer data loss due to out-of-plane head motions and head rotations (Cohn & Sayette, 2010;Lucey et al, 2011;Werner et al, 2013), although developing algorithms robust to partial face occlusions is an active area of research (Zhang et al, 2018). Another common challenge pertains to the temporal precision in the alignment of simultaneous recordings between cameras and to the stimuli.…”

Section: Selecting a Recording Methodsmentioning

confidence: 99%

FaceSync: Open source framework for recording facial expressions with head-mounted cameras

2019

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Advances in computer vision and machine learning algorithms have enabled researchers to extract facial expression data from face video recordings with greater ease and speed than standard manual coding methods, which has led to a dramatic increase in the pace of facial expression research. However, there are many limitations in recording facial expressions in laboratory settings. Conventional video recording setups using webcams, tripod-mounted cameras, or pan-tilt-zoom cameras require making compromises between cost, reliability, and flexibility. As an alternative, we propose the use of a mobile head-mounted camera that can be easily constructed from our open-source instructions and blueprints at a fraction of the cost of conventional setups. The head-mounted camera framework is supported by the open source Python toolbox FaceSync, which provides an automated method for synchronizing videos. We provide four proof-of-concept studies demonstrating the benefits of this recording system in reliably measuring and analyzing facial expressions in diverse experimental setups, including group interaction experiments.

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Facial Expression Analysis under Partial Occlusion

Cited by 919 publications

References 141 publications

Facial Expression Recognition: A Survey

Facial Expression Recognition: A Survey

Continuous Emotion Recognition in Speech — Do We Need Recurrence?

FaceSync: Open source framework for recording facial expressions with head-mounted cameras

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