Facial expression recognition using anatomy based facial graph

Mohseni, Sina; Zarei, Niloofar; Ramazani, Saba

doi:10.1109/smc.2014.6974508

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…Furthermore, some current methods select landmarks with significant contributions to avoid redundant information [75,113]. Landmarks locating external contour and nose are frequently discarded [40,76] (see Figs. 6a, b) because they are considered irrelevant to facial affects.…”

Section: Landmark-level Graphsmentioning

confidence: 99%

“…Studies of point-light displays in emotion perception also show that more complex representations seem to be redundant [117]. To this end, work like [72,74,76,115] manually reduced edges based on muscle anatomy and FACS. Another type of approach is exploiting triangulation algorithms [40], such as the Delaunay triangulation [36], to generate graph edges consistent with true facial muscle distribution and uniform for different subjects.…”

Section: Landmark-level Graphsmentioning

confidence: 99%

“…Specifically, handcrafted features (e.g., LBP [81,83], Gabor [22,115], HOG [36,87,104]) or deep-based features (e.g., CNN [74,115], VGG [91], ResNet [39]) are employed to enhance the node representation similar to many non-graph FER methods [20,31]. For reasoning approaches, early studies prefer to capture the relations of an individual node from predefined graph structures using tracking strategies (e.g., displacement projection [76] and DNG [90]) or general machine learning models (e.g., RF [40], RNN [22], CNN [36]). In the latest work, GCNs become one of the mainstream choices in the latest work and show state-of-the-art performances on posed and inthe-wild databases [39,74,81,101,105,113,115].…”

Section: Facial Expression Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

Graph-Based Facial Affect Analysis: A Review

Liu

Zhang

et al. 2023

IEEE Trans. Affective Comput.

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As one of the most important affective signals, facial affect analysis (FAA) is essential for developing human-computer interaction systems. Early methods focus on extracting appearance and geometry features associated with human affects while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. This paper provides a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their non-deep or deep learning methods, emphasizing the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.

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Section: Landmark-level Graphsmentioning

confidence: 99%

Section: Landmark-level Graphsmentioning

confidence: 99%

Section: Facial Expression Recognitionmentioning

confidence: 99%

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Graph-Based Facial Affect Analysis: A Review

Liu

Zhang

et al. 2023

IEEE Trans. Affective Comput.

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“…These included a wide variety of systems discussed in detail in Section III-C and comprise the papers used in later sections. Input only publications focused on a method of input to a robotic system such as facial recognition [16] or speech recognition [17]. Output only papers focused on robots conveying some emotion and often evaluated the output, such as audio [18] or robotic gait [19].…”

Section: B Step 2: Preliminary Sortingmentioning

confidence: 99%

A Survey of Robotics and Emotion: Classifications and Models of Emotional Interaction

Savery¹,

Weinberg²

2020

Preprint

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As emotion plays a growing role in robotic research it is crucial to develop methods to analyze and compare among the wide range of approaches. To this end we present a survey of 1427 IEEE and ACM publications that include robotics and emotion. This includes broad categorizations of trends in emotion input analysis, robot emotional expression, studies of emotional interaction and models for internal processing. We then focus on 232 papers that present internal processing of emotion, such as using a humans emotion for better interaction or turning environmental stimuli into an emotional drive for robotic path planning. We conducted constant comparison analysis of the 232 papers and arrived at three broad categorization metrics -emotional intelligence, emotional model and implementation -each including two or three subcategories. The subcategories address the algorithm used, emotional mapping, history, the emotional model, emotional categories, the role of emotion, the purpose of emotion and the platform. Our results show a diverse field of study, largely divided by the role of emotion in the system, either for improved interaction, or improved robotic performance. We also present multiple future opportunities for research and describe intrinsic challenges common in all publications.

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“…However, with recent advancements in face analysis study, improved facial landmark detection algorithms are presented in several studies. [11][12][13][14] Towards facial landmark graph features, Lei et al, 15 presented a method that only employed 28 brow and lip landmarks, which contributed significantly to micro-expressions. While, other studies [16][17][18][19] presented graph-based methods using AU to define landmarks of interest.…”

Section: Introductionmentioning

confidence: 99%

Motion and Geometric Feature Analysis for Real-time Automatic Micro-expression Recognition Systems

et al. 2021

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The trend of real-time micro-expression recognition systems has increased with recent advancements in human-computer interaction (HCI) in security and healthcare. Several studies in this field contributed towards recognition accuracy, while few studies look into addressing the computation costs. In this paper, two approaches for micro-expression feature extraction are analyzed for real-time automatic micro-expression recognition. Firstly, motion-based approach, which calculates motion of subtle changes from an image sequence and present as features. Then, secondly, a low computational geometric-based feature extraction technique, a very popular method for facial expression recognition in real-time. These approaches were integrated in a developed system together with a facial landmark detection algorithm and a classifier for real-time analysis. Moreover, the recognition performance were evaluated using SMIC, CASME, CAS(ME)2 and SAMM datasets. The results suggest that the optimized Bi-WOOF (leveraging on motion-based features) yields the highest accuracy of 68.5%, while the full-face graph (leveraging on geometric-based features) yields 75.53% on the SAMM dataset. On the other hand, the optimized Bi-WOOF processes sample at 0.36 seconds and full-face graph processes sample at 0.10 seconds with a 640x480 image size. All experiments were performed on an Intel i5-3470 machine.

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Facial expression recognition using anatomy based facial graph

Cited by 15 publications

References 18 publications

Graph-Based Facial Affect Analysis: A Review

Graph-Based Facial Affect Analysis: A Review

A Survey of Robotics and Emotion: Classifications and Models of Emotional Interaction

Motion and Geometric Feature Analysis for Real-time Automatic Micro-expression Recognition Systems

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