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Article summary:

1. This article discusses the use of deep learning algorithms to improve facial feature-based emotional recognition.

2. The paper evaluates several preprocessing techniques to demonstrate how they affect the performance of a convolutional neural network (CNN).

3. The proposed system has better accuracy than existing techniques, and it uses optimized deep learning algorithms to identify seven emotions from the Facial Action Coding System (FACS).

Article analysis:

The article is generally reliable and trustworthy, as it provides a comprehensive overview of the use of deep learning algorithms for facial feature-based emotional recognition. It also provides an evaluation of several preprocessing techniques and their effects on CNN performance. Furthermore, the article presents a proposed system that has better accuracy than existing techniques, using optimized deep learning algorithms to identify seven emotions from the Facial Action Coding System (FACS).

However, there are some potential biases in the article that should be noted. For example, while the article does provide an overview of existing techniques for facial feature-based emotional recognition, it does not explore any counterarguments or alternative approaches that could be used instead. Additionally, while the article does discuss potential risks associated with using deep learning algorithms for facial feature-based emotional recognition, it does not provide any evidence or data to support its claims about improved accuracy or effectiveness. Finally, while the article does present both sides of the argument equally, it does not provide any information about possible ethical implications or considerations when using such technology.