Model-based face tracking for view-independent facial expression recognition

Gokturk, S.B.; Bouguet, J.-Y.; Tomasi, Carlo; Girod, Bernd

doi:10.1109/afgr.2002.1004168

Cited by 69 publications

(51 citation statements)

References 18 publications

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“…Few methods have been reported for automatic AU detection in face image sequences [16]. Some researchers described patterns of facial motion that correspond to a few specific AUs but did not report on actual recognition of these AUs (e.g., [4]- [6], [8], [17], [18]). Only recently there has been an emergence of efforts toward automatic analysis of facial expressions into elementary AUs [19].…”

Section: B Automated Facs: Frontal Facementioning

confidence: 99%

Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences

Pantić

Patras

2006

IEEE Trans. Syst., Man, Cybern. B

481

283

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Abstract-Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypic emotional expressions, such as anger and happiness. Instead of representing another approach to machine analysis of prototypic facial expressions of emotion, the method presented in this paper attempts to handle a large range of human facial behavior by recognizing facial muscle actions that produce expressions. Virtually all of the existing vision systems for facial muscle action detection deal only with frontal-view face images and cannot handle temporal dynamics of facial actions. In this paper, we present a system for automatic recognition of facial action units (AUs) and their temporal models from long, profile-view face image sequences. We exploit particle filtering to track 15 facial points in an input face-profile sequence, and we introduce facial-action-dynamics recognition from continuous video input using temporal rules. The algorithm performs both automatic segmentation of an input video into facial expressions pictured and recognition of temporal segments (i.e., onset, apex, offset) of 27 AUs occurring alone or in a combination in the input face-profile video. A recognition rate of 87% is achieved.

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Section: B Automated Facs: Frontal Facementioning

confidence: 99%

Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences

Pantić

Patras

2006

IEEE Trans. Syst., Man, Cybern. B

481

283

View full text Add to dashboard Cite

show abstract

“…Geometric-based methods rely on the locations of a set of fiducial facial points [16,17], a connected face mesh [18,19], or the shapes of face components [20]. The commonly used geometric representation is facial points, which represent a face by concatenating the x and y coordinates of a number of fiducial points.…”

Section: Related Workmentioning

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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“…Facial-feature trackers [4,12,24,45] have been designed to provide video-based interfaces for people with disabilities. Eyebrow tracking is often used for determining the facial expression of the user [8,22,29]. We are not aware of any papers that address the issues of video-based communication interfaces that operate on eye blinks or eyebrow raises.…”

Section: Introductionmentioning

confidence: 99%

Communication via eye blinks and eyebrow raises: video-based human-computer interfaces

Grauman

Betke

Lombardi

et al. 2003

Universal Access in the Information Society

154

100

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Abstract. Two video-based human-computer interaction tools are introduced that can activate a binary switch and issue a selection command. "BlinkLink," as the first tool is called, automatically detects a user's eye blinks and accurately measures their durations. The system is intended to provide an alternate input modality to allow people with severe disabilities to access a computer. Voluntary long blinks trigger mouse clicks, while involuntary short blinks are ignored. The system enables communication using "blink patterns:" sequences of long and short blinks which are interpreted as semiotic messages. The second tool, "EyebrowClicker," automatically detects when a user raises his or her eyebrows and then triggers a mouse click. Both systems can initialize themselves, track the eyes at frame rate, and recover in the event of errors. No special lighting is required. The systems have been tested with interactive games and a spelling program. Results demonstrate overall detection accuracy of 95.6% for BlinkLink and 89.0% for EyebrowClicker.

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Model-based face tracking for view-independent facial expression recognition

Abstract: Facial expression recognition is necessary for designing any realistic human-machine interfaces. Previous pub-

Cited by 69 publications

References 18 publications

Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences

Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences

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

Communication via eye blinks and eyebrow raises: video-based human-computer interfaces

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