Collecting Large, Richly Annotated Facial-Expression Databases from Movies

Dhall, Abhinav; Goecke, Roland; Lucey, Simon; Gedeon, Tamás D.

doi:10.1109/mmul.2012.26

Cited by 556 publications

(276 citation statements)

References 14 publications

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“…Although CK+ and MMI are two of the most widely used datasets for evaluating facial expression recognition methods, they are both collected in a strict controlled settings with near frontal poses, consistent illumination and posed expressions. The recent and more challenging datasets of AFEW and SFEW [46] provide platforms for researchers to create, extend and test their methods on a common benchmarked data.…”

Section: Resultsmentioning

confidence: 99%

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

Fan

Tjahjadi

2017

Pattern Recognition

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A dynamic descriptor facilitates robust recognition of facial expressions in video sequences. The current two main approaches to the recognition are basic emotion recognition and recognition based on facial action coding system (FACS) action units. In this paper we focus on basic emotion recognition and propose a spatiotemporal feature based on local Zernike moment in the spatial domain using motion change frequency. We also design a dynamic feature comprising motion history image and entropy. To recognise a facial expression, a weighting strategy based on the latter feature and sub-division of the image frame is applied to the former to enhance the dynamic information of facial expression, and followed by the application of the classical support vector machine. 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-arts methods, the proposed framework demonstrates a superior performance.

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Section: Resultsmentioning

confidence: 99%

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

Fan

Tjahjadi

2017

Pattern Recognition

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“…• AFEW Database: The Acted Facial Expression in Wild (AFEW) database [38] has been collected from movies which depicts or simulates the spontaneous expressions in uncontrolled environments. The database consists of three sets: training, validation and test.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

Natural Facial and Head Behavior Recognition using Dictionary of Motion Primitives

Shi

Ukita

Yang

2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a natural facial and head behavior recognition method using hybrid dynamical systems. Most existing facial and head behavior recognition methods focus on analyzing deliberately displayed prototypical emotion patterns rather than complex and spontaneous facial and head behaviors in natural conversation environments. We first capture spatio-temporal features on important facial parts via dense feature extraction. Next, we cluster the spatio-temporal features using hybrid dynamical systems, and construct a dictionary of motion primitives to cover all possible elemental motion dynamics accounting for facial and head behaviors. With this dictionary, the facial and head behavior can be interpreted into a distribution of motion primitives. This interpretation is robust against different rhythms of dynamic patterns in complex and spontaneous facial and head behaviors. We evaluate the proposed approach under natural tele-communication scenarios, and achieve promising results. Furthermore, the proposed method also performs favorably against the state-ofthe-art methods on three benchmark databases.

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“…For the AFEW DB, which is a much more challenging database, evaluation experiments also have been done [11]. All of the AFEW sets were collected from movies to depict the so-called wild scenarios.…”

Section: Afew Dbmentioning

confidence: 99%

“…We experimentally evaluated the proposed method in three public expression databases, i.e., the Extended Cohn-Kanade (CK+) [8], MMI Facial Expression Database [9,10], and Annotated Facial Landmarks in the Wild (AFEW) database [11], that together represent labcontrolled and real-world scenarios. Some examples of expression recognition results are shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Multithreading cascade of SURF for facial expression recognition

Chen

Luo

Takiguchi

et al. 2016

J Image Video Proc.

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We propose a novel and general framework called the multithreading cascade of Speeded Up Robust Features (McSURF), which is capable of processing multiple classifications simultaneously and accurately. The proposed framework adopts SURF features, but the framework is a multi-class and simultaneous cascade, i.e., a multithreading cascade. McSURF is implemented by configuring an area under the receiver operating characteristic (ROC) curve (AUC) of the weak SURF classifier for each data category into a real-value lookup list. These non-interfering lists are built into thread channels to train the boosting cascade for each data category. This boosting cascade-based approach can be trained to fit complex distributions and can simultaneously and robustly process multi-class events. The proposed method takes facial expression recognition as a test case and validates its use on three popular and representative public databases: the Extended Cohn-Kanade, MMI Facial Expression Database, and Annotated Facial Landmarks in the Wild database. Overall results show that this framework outperforms other state-of-the-art methods.

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Collecting Large, Richly Annotated Facial-Expression Databases from Movies

Cited by 556 publications

References 14 publications

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

A dynamic framework based on local Zernike moment and motion history image for facial expression recognition

Natural Facial and Head Behavior Recognition using Dictionary of Motion Primitives

Multithreading cascade of SURF for facial expression recognition

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