Self-adaptive expert system for facial expression analysis

Pantić, Maja; Rothkrantz, Léon J. M.

doi:10.1109/icsmc.2000.884967

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…The works of facial expression had been implement in many area such as security, biometric, robotic and Human Computer Interaction and [12] had suggest the ideal system that all of stages of facial expression analysis are to be performed automatically from face detection to facial expression information extraction and facial expression classification. The characteristics of automatic facial expression classifier are also mentioned as in Table 1 and Table 2.…”

Section: Related Workmentioning

confidence: 99%

Facial Expression as an Implicit Customers' Feedback and the Challenges

Kasiran

Yahya

2007

Computer Graphics, Imaging and Visualisation (CGIV 2007)

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The human face is rich of information and plays important roles in daily communication such as expressing the emotions nonverbally. Facial expression comes in all varieties. Some are intense and sustained while others are subtle and fleeting. Much progress has been made to build computer systems that recognize facial expression for human computer interaction such as affective computing which apply the automatic facial recognition techniques in human computer interaction where the main idea is that the computer could better adjust its behavior to user's current emotion. Other possible area that could use the advance technology of Facial expression recognition system is the customer satisfaction measurement. The expression of customer being served at the counter is captured to evaluate the satisfaction of the customer. This multimedia approach of customer satisfaction measurement is an alternative of the conventional way of collecting customers' response.

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Section: Related Workmentioning

confidence: 99%

Facial Expression as an Implicit Customers' Feedback and the Challenges

Kasiran

Yahya

2007

Computer Graphics, Imaging and Visualisation (CGIV 2007)

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“…Though in a different context, expert systems have previously been used for facial expression analyses. For example, in [29] a selfadaptive expert system used the facial feature contours localized in a static dual-view facial image to label the interpreted facial expressions. Authors [30] deployed a belief rule-based expert system that exploited the outputs of a CNN classifier for inferring the mental state of a person using the observed facial expressions.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Separation and Classification Network for Dynamic Microexpression Classification

Vice,

Khan,

Tan

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Macrolevel facial muscle variations, as used for building models of seven discrete facial expressions, suffice when distinguishing between macrolevel human affective states but won't discretise continuous and dynamic microlevel variations in facial expressions. We present a hierarchical separation and classification network (HSCN) for discovering dynamic, continuous, and macro-and microlevel variations in facial expressions of affective states. In the HSCN, we first invoke an unsupervised cosine similarity-based separation method on continuous facial expression data to extract twenty-one dynamic facial expression classes from the seven common discrete affective states. The between-clusters separation is then optimized for discovering the macrolevel changes resulting from facial muscle activations. A following step in the HSCN separates the upper and lower facial regions for realizing changes pertaining to upper and lower facial muscle activations. Data from the two separated facial regions are then clustered in a linear discriminant space using similarities in muscular activation patterns. Next, the actual dynamic expression data are mapped onto discriminant features for developing a rule-based expert system that facilitates classifying twenty-one upper and twenty-one lower microexpressions. Invoking the random forest algorithm would classify twenty-one macrolevel facial expressions with 76.11% accuracy. A support vector machine (SVM), used separately on upper and lower facial regions in tandem, could classify them with respective accuracies of 73.63% and 87.68%. This work demonstrates a novel and effective method of dynamic assessment of affective states. The HSCN further demonstrates that facial muscle variations gathered from either upper, lower, or full-face would suffice classifying affective states. We also provide new insight into discovery of microlevel facial muscle variations and their utilization in dynamic assessment of facial expressions of affective states.

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“…The use of an expert system in facial expression recognition systems has been applied in several previous works. For example, in [15] a self-adaptive expert system uses the facial feature contours localized in a static dualview facial image to label the interpreted facial expression. In [16], the authors deploy a 'Belief Rule-Based Expert System' that exploits the outputs of a convolutional neural network (CNN) classifier to infer the mental state of a person based on their facial expression.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Separation and Classification Network for Dynamic Micro-Expression Classification

Vice¹,

Khan²,

Tan³

et al. 2023

Preprint

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<p> Models of seven discrete facial expressions are built on macro-level facial muscle variations for separating distinct affective states. We propose a step-wise Hierarchical Separation and Classification Network (HSCN) that discovers dynamic and continuous macro- and micro-level variations in facial expressions. The HSCN first invokes an unsupervised cosine similarity-based separation method on continuous facial expression data and extracts twenty-one dynamic expression classes from the seven common discrete affective states. Separation between the clusters is then optimised for discovering the macro-level changes in facial muscle activations followed by splitting the upper and lower facial regions for realising and modelling changes pertaining to upper and lower facial muscle activations. A linear discriminant space is developed for clustering the upper and lower facial images on the basis of similar muscular activation patterns. Actual dynamic data and linear discriminant features are mapped for developing a rule-based expert system that would facilitate classification of twenty-one upper and twenty-one lower facial micro-expressions. Using the random forest algorithm, classification accuracies of 76.11\% were observed for dynamic macro-level facial expression classification. A support vector machine provided 73.63\% and 87.68\% accuracies respectively while classifying upper and lower facial micro-expressions. This work provides a novel framework for the dynamic assessment of affective states. Reported methods and results also provide new insight into the dynamic analysis of facial expressions of affective states. </p>

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Self-adaptive expert system for facial expression analysis

Cited by 8 publications

References 17 publications

Facial Expression as an Implicit Customers' Feedback and the Challenges

Facial Expression as an Implicit Customers' Feedback and the Challenges

A Hierarchical Separation and Classification Network for Dynamic Microexpression Classification

A Hierarchical Separation and Classification Network for Dynamic Micro-Expression Classification

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