Retraction Note to: Real-Life Facial Expression Recognition Systems: A Review

Goyal, Samta Jain; Upadhyay, Arvind Kumar; Jadon, R. S.; Goyal, Rajeev

doi:10.1007/978-981-10-5544-7_74

Cited by 4 publications

(6 citation statements)

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“…Unlike most studies based on existing images or videos, Deshmukh et al [31] summarise the latest advances in the algorithms and techniques used in distinct phases of real-time FER. Some real-life challenges of FER systems are proposed in [32,33].…”

Section: Differences With Existing Survey and Contributionsmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches. Methodologically, to highlight the differences and similarities, we propose a general framework of a conventional FER approach and review the possible technologies that can be employed in each component. As for deep learning-based methods, four kinds of neural network-based state-of-the-art FER approaches are presented and analysed. Besides, we introduce seventeen commonly used FER datasets and summarise four FER-related elements of datasets that may influence the choosing and processing of FER approaches. Evaluation methods and metrics are given in the later part to show how to assess FER algorithms, along with subsequent performance comparisons of different FER approaches on the benchmark datasets. At the end of the survey, we present some challenges and opportunities that need to be addressed in future.

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Section: Differences With Existing Survey and Contributionsmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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“…It is important for developers to release systems that are designed and built using enough real-world, spontaneous facial expression data [94]. The number of facial expressions used for training and developing FEA, FAM, and FSA systems should be much higher to lead to more realistic results.…”

Section: Real-world Spontaneous Data Collectionmentioning

confidence: 99%

“…In case of having a low number of images for training, it is challenging to choose the best approaches to enlarge the dataset while developing the system. Expressive robot developers also need to make sure the system includes a continuous adoption process that learns each user's expressions over time and adds them to its knowledge base [94]. It is also important to pay close attention to include the variability of the facial data in terms of subjects by including data from subjects well represented in gender and ethnicity, as well as diversity in terms of lighting, head position, and face resolution [94].…”

Section: Real-world Spontaneous Data Collectionmentioning

confidence: 99%

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Pourebadi

Riek

2022

ACM Trans. Comput. Healthcare

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Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.

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“…The recognition process in computer vision is based on two crucial steps: feature extraction and classification. The challenges that arise when building such recognition systems [2] relate to the choice of the feature extraction technique that helps discriminating the object from the rest of the image and the classification technique that allows identifying the class to which the object belongs. Image databases are provided by psychologists in the format of video sequence recordings [3] and images [4] of a set of participants showing different classes of emotional expression, such as expressions of joy, surprise, fear, anger, sadness, disgust and the neutral face.…”

Section: Introductionmentioning

confidence: 99%

Tuning Image Descriptors and Classifiers: The Case of Emotion Recognition

Greche

Taamouch

Akil

et al. 2021

Lecture Notes in Electrical Engineering

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In general, the recognition involves several steps as follows: data acquisition, pre-processing, segmentation, feature extraction and classification. Automatic facial expression recognition has become a crucial technology in the computer vision field and its applications including identification and security, Medicine and Monitoring. The facial expression recognition system requires an algorithmic pipeline that involves two main blocks: feature extraction and classification. A large experimental session must lead to the adequate algorithmic pipeline, notably for identifying the best methods for feature extraction and classification to achieve robust facial expression recognition with high accuracy. Thus, it is essential to analyse data using multiple methods of feature extraction and classification. In this paper, we propose an approach to automate the analysis of data by repeating tests made to tune and compare feature extraction and classification methods. We evaluate our proposed data analysis approach using video sequences with fundamental emotion states: neutral expression, disgust, fear, happiness, sadness, anger and surprise. To transform the face images into vectors of features, we use shape, texture, and contour descriptors. This enables storing images in a table of vectors. Each table related to every descriptor is analysed with Five classifiers have been used, which are support vector machine, linear discriminant analysis, k-nearest neighbors, naïve Bayes, and binary tree classifiers. The techniques 10-fold and Leave-One-Out Cross-validations and the grid search have been used to tune the hyperparameters methods and compare them, computing the average recognition rate F-measure as evaluation metric. Experimentation on ChonKanade Image database (CK+) show that the proposed data analysis approach can find out the optimal combination to separate the data classes and identify the expression with a an F-score of 96.44%.

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Retraction Note to: Real-Life Facial Expression Recognition Systems: A Review

Cited by 4 publications

References 0 publications

Facial Expression Recognition: A Survey

Facial Expression Recognition: A Survey

Facial Expression Modeling and Synthesis for Patient Simulator Systems: Past, Present, and Future

Tuning Image Descriptors and Classifiers: The Case of Emotion Recognition

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