Summary of experience in developing online face recognition system based on C#

1. Introduction

With the development of artificial intelligence and big data technology, face recognition technology has gradually become indispensable in life. a technology. The emergence of online face recognition systems provides a more convenient way to apply this technology. This article introduces the author's experience summary in developing an online face recognition system based on C#.

2. Implementation method

2.1 Face detection

The first step in face recognition is face detection. In this project, we used the open source face detection algorithm OpenCV, which uses the Haar feature classifier for face detection and showed good results in the experiment.

2.2 Facial feature extraction

After obtaining the face detection results, we need to further extract the facial features. In this project, we used the FaceNet algorithm to extract features from faces. This algorithm uses a deep learning model for face recognition, and its accuracy is relatively high.

2.3 Face recognition

After obtaining the face feature vector, we need to compare it with the samples in the existing face feature database to achieve the accuracy of face recognition. Effect. In this project, we used the k-nearest neighbor algorithm for face recognition, and used Euclidean distance as a measure of similarity.

3. Implementation details

3.1 Database management

When performing face recognition, it is necessary to use the existing facial feature database for sample comparison. Therefore, a database management module needs to be designed to manage and store facial feature vectors and other related data.

3.2 Front-end interface design

In order to facilitate user use and experience, a friendly front-end interface design is required. In this project, we used WPF technology for front-end interface design and implemented it using the MVVM framework, making the interface design and logic processing clearer.

3.3 System security design

Since the system involves sensitive information such as user privacy, system security design is required. In this project, we used secure encryption technologies such as HTTPS, and at the same time conducted identity verification and authorization management for users in login, registration and other aspects to ensure the security and reliability of the system.

4. Project effect

In the experiment, we used more than 5,000 face pictures for sample training, and tested about 1,000 faces on the system pictures, and finally achieved a more satisfactory face recognition effect. At the same time, the system also has better interface design and user experience.

5. Summary

This article introduces the development process and experience summary of an online face recognition system based on C#, including face detection, feature extraction, recognition algorithm selection, etc. aspect. At the same time, it focuses on the implementation details such as data management, front-end interface design and system security design. In the experiment, it achieved better face recognition results and had better user experience.

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