Facial health feature recognition system implemented in Python

introduction

This article will introduce a facial health feature identification system based on Python. The system uses public data sets obtained from the Internet and divides them into three categories: healthy, sub-healthy and unhealthy. The system mainly uses the Python programming language, Flask framework and MobileNet model. Functions include model training and front-end recognition testing interfaces, which include functions for image uploading and recognition result return. The model training phase includes multiple steps such as reading images, processing images, model training, and using the trained model for image recognition.

1. Data set acquisition and preparation

First, we will introduce how to obtain facial health feature data sets from the Internet, including three categories: healthy, sub-healthy and unhealthy. The data set contains 1,000 facial expression images and 1,000 tongue images, providing sufficient samples for model training.

2. Model training

In this section, we will explain the model training process in detail. Including how to read image data, perform image processing, use the MobileNet model for training, and save the trained model.

3. Application of Flask framework

Using the Flask framework, we will show how to build a front-end recognition test interface. The interface includes an image upload function, allowing users to upload facial images to be recognized. After uploading, the system will call the trained model for image recognition and return the recognition results.

4. Front-end recognition test interface

We will introduce the system's front-end recognition test interface, show how users can upload facial images through simple operations, and view the facial health feature identification results returned by the system. The friendliness of this interface makes the system easier to use.

Conclusion and Outlook

Through the introduction of this article, readers will have a comprehensive understanding of the facial health feature identification system based on Python. The system not only has the function of model training, but also provides an easy-to-use front-end recognition test interface. In the future, the system can be further expanded to add more facial feature discriminations, providing more possibilities for research and practice in the health field.