Learning spatial weighting for facial expression analysis via constrained quadratic programming

Liao, C.-Y.; Chuang, Hui-Ju; Duan, Chih-Hsueh; Lai, Shang-Hong

doi:10.1016/j.patcog.2013.03.017

Cited by 12 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AdaBoost and GentleBoost [41] are the most widely employed boosting techniques. In addition to generic feature selection techniques, approaches tailored to affect recognition are also developed, for example to learn informative spatial regions by observing the temporal evolution of expressions [74].…”

Section: Feature Selectionmentioning

confidence: 99%

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

Sarıyanidi

Güneş

Cavallaro

2015

IEEE Trans. Pattern Anal. Mach. Intell.

599

372

View full text Add to dashboard Cite

Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them. In this paper, we review the progress across a range of affect recognition applications to shed light on these fundamental questions. We analyse the state-of-the-art solutions by decomposing their pipelines into fundamental components, namely face registration, representation, dimensionality reduction and recognition. We discuss the role of these components and highlight the models and new trends that are followed in their design. Moreover, we provide a comprehensive analysis of facial representations by uncovering their advantages and limitations; we elaborate on the type of information they encode and discuss how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias. This survey allows us to identify open issues and to define future directions for designing real-world affect recognition systems.

show abstract

Section: Feature Selectionmentioning

confidence: 99%

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

Sarıyanidi

Güneş

Cavallaro

2015

IEEE Trans. Pattern Anal. Mach. Intell.

599

372

View full text Add to dashboard Cite

show abstract

“…Compared to optical flow approaches [15,46] and handcrafted approaches [47,50,32,48,49], our method based only on optical flow obtains competitive results (96.94%). Despite the noise contained in the original optical flows, the variation in sequence length and expression activation patterns, the joint analysis of magnitudes and orientations keeps only the pertinent motion.…”

Section: Macro Expressionmentioning

confidence: 93%

“…Therefore, a dynamic extension of LBP called Local Binary Pattern on Three Orthogonal Plans (LBP-TOP) is proposed by [14]. Considering the latest developments in dynamic texture domains, the optical flow have regained interest from the community becoming one of the most widely used and recognized solution [15,16]. Optical flow methods are popular, because they estimate in a natural way the local dynamics and temporal texture characteristics.…”

Section: Macro Expression Recognitionmentioning

confidence: 99%

Advanced local motion patterns for macro and micro facial expression recognition

Allaert,

Bilasco,

Djeraba

2018

Preprint

View full text Add to dashboard Cite

In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative local motion patterns feature, with three main contributions. The first one is the analysis of the face skin temporal elasticity and face deformations during expression. The second one is a unified approach for both macro and micro expression recognition. And, the third one is the step forward towards in-the-wild expression recognition, dealing with challenges such as various intensity and various expression activation patterns, illumination variation and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-rank state-of-the-art methods for macro expression recognition.

show abstract

“…Nowadays, human life is being incorporated with intelligence, and the key problem to be solved in intelligent life is to explore the true inner world and emotional expressions of human beings. Facial feature extraction in FER integrates digital image processing [13], [14], biology [15], [16], and statistical theory [17], [18]. Firstly the features of computer-processed facial expression images are extracted and represented by statistical models to complete modeling of expression feature.…”

Section: Introductionmentioning

confidence: 99%

WGAN-Based Robust Occluded Facial Expression Recognition

et al. 2019

View full text Add to dashboard Cite

Research on facial expression recognition (FER) technology can promote the development of theoretical and practical applications for our daily life. Currently, most of the related works on this technology are focused on un-occluded FER. However, in real life, facial expression images often have partial occlusion; therefore, the accurate recognition of occluded facial expression images is a topic that should be explored. In this paper, we proposed a novel Wasserstein generative adversarial network-based method to perform occluded FER. After complementing the face occlusion image with complex facial expression information, the recognition is achieved by learning the facial expression features of the images. This method consists of a generator G and two discriminators D 1 and D 2. The generator naturally complements occlusion in the expression image under the triple constraints of weighted reconstruction loss l wr , triplet loss l t , and adversarial loss l a. We optimize the discriminator D 1 to distinguish between real and fake by constructing an adversarial loss l a between the generated complementing images, original un-occluded images, and smallscale-occluded images based on the Wasserstein distance. Finally, the FER is completed by introducing classification loss l c into D 2. To verify the effectiveness of the proposed method, an experimental analysis was performed on the AffectNet and RAF-DB datasets. The visual occlusion complementing results, comparison of recognition rates of facial expression images with and without de-occlusion processing, and T-distributed stochastic neighbor embedding visual analysis of facial expression features all prove the effectiveness of the proposed method. The experimental results show that the proposed method is better than the existing state-of-the-art methods. INDEX TERMS Facial expression recognition, partial occlusion, image complementation, Wasserstein generative adversarial network.

show abstract

Learning spatial weighting for facial expression analysis via constrained quadratic programming

Cited by 12 publications

References 30 publications

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

Advanced local motion patterns for macro and micro facial expression recognition

WGAN-Based Robust Occluded Facial Expression Recognition

Contact Info

Product

Resources

About