Review Of Emotion Recognition Technology Methods Based on Deep Recognition

Zhizhou Lu

doi:10.54097/hq3rzm08

Authors

Zhizhou Lu

DOI:

https://doi.org/10.54097/hq3rzm08

Keywords:

emotion recognition; reducing recognition errors; human-computer interaction; deep learning technology.

Abstract

With the rapid advancement of artificial intelligence, sensor technology, and big data analytics, the way humans interact with machines is shifting from an "instruction-based" mode to a "perception-based" one. As emotions are the core driving force behind human behaviors and decisions, the objective and quantitative detection of emotions has become a focus of interdisciplinary research. Studies have shown that emotions can be detected and analyzed through four methods: facial expression recognition, speech emotion recognition, text sentiment analysis, and physiological signal recognition. Reducing errors in emotion recognition is of great help to the development and widespread application of human-computer interaction. The development and research on emotion detection is not only an inevitable outcome of technological development but also a key to addressing practical needs. This paper will analyze deep learning models such as CNN, LSTM, and SENN, and summarize their advantages and disadvantages. It breaks the traditional perception that "emotions cannot be quantified," enabling machines to move from "understanding language" to "understanding the human heart," and ultimately promoting the harmonious coexistence of technology and human society.

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