The rapid development of artificial intelligence is inseparable from the support of high-quality data. Data is not only the fuel that drives the progress of AI model learning, but also the core factor to improve model performance, accuracy and stability. Especially in the field of automatic tasks and intelligent decision-making, deep learning algorithms based on massive data have shown their potential. Therefore, having well-structured and rich datasets has become a top priority for engineers and developers to ensure that AI systems can perform well in a variety of different scenarios.

Principles of Emotion Recognition

Facial Action Coding System (FACS) provides theoretical support for machines to judge human emotions through face recognition. First, the machine digitizes the relevant information of the face image. After the machine recognizes the face, it will extract the key muscle points of the face. The function of these key points is to label the facial features and contours to perform the positioning of the key points (eyes, mouth, nose, etc.) of the face. The more key points extracted by the machine, the higher the recognition accuracy and the higher the corresponding performance requirements.

Different human expressions are actually changes in the position of these key points (relative position, absolute position, distance and angle, etc.), and then these changes are compared with the digitalized Facial Action Coding System (FACS). In this way, human emotions are recognized by the machine.

Application of Emotion Recognition

In the field of retail, emotion detection can identify customer satisfaction levels, thereby changing marketing strategies, and at the same time help customers who are dissatisfied, confused or hesitant, solve their difficulties and improve their shopping experience.

In the field of marketing, the effect of an advertisement is unknown before it is launched. At this time, emotion recognition can play a role. For example, if the audience of an advertisement is young people, then the advertiser can select some samples in advance, judge by emotion recognition whether the advertisement could achieve the effect and improve the advertisement in reference of the results of emotion recognition.

Emotion Recognition Data Solution

Nexdata has developed the “ 25,998 People Multi-race 7 Expressions Recognition Data”. Nexdata strictly abides by the relevant regulations, and the data is collected with proper data collection authorization agreement. Nexdata is committed to promoting the emotion of emotion recognition technology with high-quality data and fully ensuring the data security.

25,998 People Multi-race 7 Expressions Recognition Data

25,998 people, including Asian, Caucasian people, brown people and black people. 7 images for each people, total 181,986 images. The accuracy is over 97% based on the accuracy of the expressions.

Emotion recognition can help machine recognize human emotions and give the most appropriate feedback. With the continuous development of technology, there are more and more researches on emotion recognition. In the future, emotion recognition will play an important role in the application field of human-machine interaction.

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The future of AI is highly dependent on the support of data. With the development of technology and the expansion of application scenarios, high-quality datasets will become the key point to promoting AI performance. In this data-driven revolution, we will be able to better meet the opportunities and challenges of technology development if we constantly focus on data quality and strengthen data security management.