A novel approach for facial expression recognition

Boughida, Adil; Nadjib, Kouahla Mohamed; Lafifi, Yacine

doi:10.1109/icnas.2019.8807883

Cited by 12 publications

(2 citation statements)

References 26 publications

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“…Many methods have been exploited as extraction features tool: Ershad et al [11] used Dispelling Classes Gradually (DCG) concept on the input data to reduce features, Siouda et al [12] exploited the deep autoencoder as feature extractor tool. • Feature selection choose the most important or the relevant features that can improve the classification quality, as an example of the feature selection methods: Boughida et al [13] that used Principal Component Analysis (PCA) for selecting the best features which represent more the facial expression. In the same context and for improving results quality authors of [14] exploited genetic algorithm to select the best features and to optimize SVM hyperparameters.…”

Section: Related Workmentioning

confidence: 99%

Revised Artificial Immune Recognition System

et al. 2021

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Artificial Immune Recognition System is a widely used bio-inspired algorithm that describes the recognition tasks of antigen by memory cells. Despite the success of the Artificial Immune Recognition System, the basic version has some drawbacks which have a direct impact on system efficiency in terms of the quality of the results, data explosion, and calculation cost. This paper investigates these disadvantages and proposes several modifications in the original version to overcome these problems. First, the concept of weight and lifetime counter was introduced for each memory cell to improve quality; second, a new mechanism was added to eliminate inactive memory cell models to reduce data explosion, and third, the structure of the memory cells set was replaced by a binary search tree to reduce processing time. Furthermore, this paper improves some algorithm functionalities especially in the mutation function and the memory cell introduction mechanism. The experimental results conducted on eleven public datasets show that the proposed method outperforms the original version, all the revised versions, and achieved a good rank compared to the other state-of-the-art methods with an average accuracy of 93.20 % on all tested datasets.

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Section: Related Workmentioning

confidence: 99%

Revised Artificial Immune Recognition System

et al. 2021

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“…Spatial PCA is applied to pixels and the temperature values to decide the relationships between the various temperature values measured for each pixel. Other recent research works acquired PCA for purposes like face recognition [25], dimensionality reduction of the feature space [26], and feature selection [27] which are different in nature of the application than frame selection by mapping into eigenspace.…”

Section: Related Researchmentioning

confidence: 99%