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Adaptive increasing-margin adversarial neural iterative system based on facial expression recognition feature models

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Abstract

Face expression recognition has been proved as a challenging task in image processing. Many related works on facial expression recognition had done but they faced several challenges during the classification of data with the stored database. It carried out various workflows on improvisation of classifiers based on deep learning but they have been lagging in understanding facial expression mainly because of disastrous forgetting, time management, data mixing, and data overfitting, etc. Ignoring all these challenges would lead to inaccurate recognition of facial expressions. Hence to overcome all the above issues this work proposed a model named adaptive increasing-margin adversarial neural iterative model involves triple threat filtration techniques along with modified scaling density-based spatial clustering of applications with noise and dual feature model for obtaining a better quality featured image. Advance back propagation artificial neural network model is initiated to overcome catastrophic forgetting, underfitting of data, over fitting of data, etc. Thus, the proposed work achieves better efficiency as well as high accuracy in terms of facial expression recognition.

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  1. Vasireddy Venkatadri Institute of Technology, Nambur, Pedakakani Mandal, Guntur District, Andrapradhesh, 522008, India

    Ramachandran Vedantham

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  1. Ramachandran Vedantham
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Correspondence to Ramachandran Vedantham.

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Vedantham, R. Adaptive increasing-margin adversarial neural iterative system based on facial expression recognition feature models. Multimed Tools Appl 81, 3793–3830 (2022). https://doi.org/10.1007/s11042-021-11320-1

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