Human Facial Expression Recognition using a 3D Morphable Model

Ramanathan, S.; Kassim, Ashraf A.; Venkatesh, Y. V.; Wah, Win

doi:10.1109/icip.2006.312417

Cited by 45 publications

(21 citation statements)

References 11 publications

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“…Ramanathan et al [26]] used the morphological expression model to fit the different face, and achieved the recognition rate of 97.0%, but the expression database used included only these three expressions: "happy", "sad "," Angry ". Savran et al used the SFAM to fit the face, and the shape index was encoded by multi-scale LBP as the feature vector, and the recognition rate was 87.2%.…”

Section: Methods Based On Probabilistic Modelmentioning

confidence: 99%

New Advance of Feature Extraction Algorithm for FER

Zhang¹

2017

dtcse

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Abstract. As a challenging interdisciplinary in biometrics and emotional computing, Facial expression recognition (FER) has become a research hotspot in the field of pattern recognition, computer vision and artificial intelligence both at home and abroad. The extraction and selection of facial expression features is one of the most important steps in the process of FER, The validity of the features extracted directly affect the performance of FER. This paper focus on the analysis of the current research states of the latest facial expression extraction algorithm based the twodimensional and the three-dimensional, try to analyze and compare the various methods in theory, and comprehensively study facial expression recognition based the convolution neural network. Finally, the research challenges are generally concluded, and the possible trends are outlined.

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Section: Methods Based On Probabilistic Modelmentioning

confidence: 99%

New Advance of Feature Extraction Algorithm for FER

Zhang¹

2017

dtcse

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“…Some systems assume the availability of neutral face images in case of expression classification (e.g. [15]) or assume that the probes and the gallery images are neutral in case of face recognition. Due to the complexity of the variations in facial expressions and the human face, these features are still believed to exhibit some sensitivities to these variations.…”

Section: Related Workmentioning

confidence: 99%

On Decomposing an Unseen 3D Face into Neutral Face and Expression Deformations

Al-Osaimi

Bennamoun

Mian

2009

Advances in Biometrics

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Abstract. This paper presents a technique for decomposing an unseen 3D face under any facial expression into an estimated 3D neutral face and expression deformations (the shape residue between the non-neutral and the estimated neutral 3D face). We show that this decomposition gives a robust facial expression classification and improves the accuracy of an off-the-shelf 3D face recognition system. The proposed decomposition system is a multistage data-driven process in which training expression residues and neutral faces reciprocally guide the decomposition of the 3D face. A plausible decomposition was achieved. The shapes and the normals of the expression residue are used for expression classification while the neutral face estimates are used for expression robust face recognition. Experiments were performed on a large number of non-neutral scans and significant expression classification rates were achieved. Moreover, 6% increase in face recognition rate was achieved for probes with severe facial expressions.

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“…Ramanathan et al [35] adapted Shelton's algorithm [63] to obtain correspondence between textured 3D meshes. A Morphable Expression Model (MEM) was then created which incorporates expression-dependent face variations in terms of morphing vectors.…”

Section: B Model-based Methodsmentioning

confidence: 99%

3D facial expression recognition: A perspective on promises and challenges

Fang¹,

Zhao²,

Ocegueda³

et al. 2011

Face and Gesture 2011

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Abstract-This survey focuses on discrete expression classification and facial action unit recognition performed using 3D face data, possibly including a corresponding 2D texture image. Research trends to date are summarized and the limitations of current methods are discussed. The challenges towards the development of more accurate and automated 3D facial expression recognition methods are identified. We also call for standardized experimental protocols in order to draw fair and meaningful comparisons between different systems.

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Human Facial Expression Recognition using a 3D Morphable Model

Cited by 45 publications

References 11 publications

New Advance of Feature Extraction Algorithm for FER

New Advance of Feature Extraction Algorithm for FER

On Decomposing an Unseen 3D Face into Neutral Face and Expression Deformations

3D facial expression recognition: A perspective on promises and challenges

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