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使用OpenCV进行人脸识别的毕业论文
2023-10-02 03:13:38 深夜i     20     0
OpenCV 人脸识别 毕业论文 图像处理 特征提取

Title: Face Recognition using OpenCV: A Graduation Thesis

Introduction:

Face recognition systems have gained significant attention in the field of computer vision and image processing. The ability to automatically detect and identify human faces from images or videos holds great potential in various applications, including security systems, surveillance, and human-computer interaction. In this graduation thesis, we explore the implementation of face recognition using OpenCV, a widely-used open-source computer vision library.

Methodology:

To achieve accurate face recognition, we follow a systematic approach that involves several steps. Firstly, we collect a diverse dataset of face images, consisting of samples from different angles, lighting conditions, and facial expressions. This dataset serves as the foundation for training our face recognition model.

Next, we preprocess the collected images by detecting and aligning the faces using OpenCV's pre-trained face detection algorithms. This step ensures that all faces in the dataset are properly oriented and normalized, reducing the impact of variations in pose and appearance.

We then extract relevant facial features from the preprocessed images. OpenCV provides several feature extraction methods, including Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Eigenfaces. These extracted features serve as the high-dimensional representation of each face, capturing unique facial characteristics.

Once the feature extraction is complete, we utilize machine learning algorithms to train a face recognition model. OpenCV offers a range of popular classifiers, such as Support Vector Machines (SVM) and Neural Networks, which can be used for this purpose. The trained model learns to distinguish between different individuals based on their facial features.

Results and Evaluation:

To evaluate the performance of our face recognition system, we conduct experiments using a separate test dataset. We measure crucial metrics such as accuracy, precision, and recall to assess the system's ability to correctly recognize and classify faces. Additionally, we compare the results of different feature extraction methods and classifiers to determine the optimal combination.

Discussion and Conclusion:

The results of our experiments demonstrate the effectiveness of using OpenCV for face recognition tasks. With careful dataset collection, preprocessing, and feature extraction, we achieve promising accuracy rates. The choice of feature extraction method and classifier greatly influences the system's performance, highlighting the importance of selecting appropriate algorithms.

Overall, this graduation thesis presents a comprehensive study on face recognition using OpenCV. It provides valuable insights into the process of collecting, preprocessing, and extracting facial features from image datasets, along with training and evaluating a face recognition model. The findings of this research can contribute to the development of more robust and accurate face recognition systems, which have wide-ranging implications in various domains.

  
  

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