[Computer Vision] Fingerprint recognition position and attitude algorithm based on texture features

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Introduction

Introduction

Position and attitude estimation in computer vision texture feature-based fingerprint recognition refers to the process of determining the position and orientation of a fingerprint in a fingerprint image. This information is very important for subsequent feature extraction and matching. Two commonly used algorithms are introduced below to achieve position and attitude estimation: direction map and bounding box.

Orientation Map

Orientation Map is used to represent the orientation information of each pixel in the fingerprint image. Orientation map is very important for fingerprint recognition tasks. It can reflect the direction and direction of fingerprint lines and is used for subsequent feature extraction and matching processes.

1. Calculate the gradient

a. 对指纹图像应用梯度算子（如Sobel、Prewitt等），得到x方向和y方向上的梯度图像。
b. 梯度图像表示图像中每个像素的变化率，即在x和y方向上的亮度变化。

2. Calculate the magnitude and direction of the gradient

a. 对于每个像素，计算其梯度的幅值（即梯度大小）和方向角度。
b. 梯度幅值可以通过计算欧氏距离或其他方式获得。
c. 梯度方向角度可以通过计算每个像素的反正切得到。

3. Create a direction map

a. 对于方向图中的每个像素，将其方向角度映射到相应的位置。
b. 可以使用不同的颜色编码方案来表示方向图，如色轮表示不同的方向或灰度级表示。

angle = arctan2(y_gradient, x_gradient)

方向图的生成后，可以通过可视化来观察指纹图像中的方向信息，帮助确定指纹纹线的方向和旋转角度。
方向图还可以用于指纹图像的预处理，如指纹图像增强、方向纹线的提取等。

Python

def calculate_orientation_map(image):
    # 计算图像的梯度
    gradient_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3)
    gradient_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3)

    # 计算梯度的幅值和方向
    gradient_magnitude = np.sqrt(gradient_x ** 2 + gradient_y ** 2)
    gradient_angle = np.arctan2(gradient_y, gradient_x)

    # 将角度转换为0到180度的范围
    gradient_angle = np.rad2deg(gradient_angle) % 180

    return gradient_angle

# 读取指纹图像
fingerprint_image = cv2.imread('fingerprint.png', cv2.IMREAD_GRAYSCALE)

# 计算方向图
orientation_map = calculate_orientation_map(fingerprint_image)

C++

cv::Mat calculateOrientationMap(const cv::Mat& image) {
    
    
    // 计算图像的梯度
    cv::Mat gradient_x, gradient_y;
    cv::Sobel(image, gradient_x, CV_64F, 1, 0, 3);
    cv::Sobel(image, gradient_y, CV_64F, 0, 1, 3);

    // 计算梯度的幅值和方向
    cv::Mat gradient_magnitude, gradient_angle;
    cv::cartToPolar(gradient_x, gradient_y, gradient_magnitude, gradient_angle, true);

    // 将角度转换为0到180度的范围
    gradient_angle = gradient_angle * 180 / CV_PI;

    return gradient_angle;
}

The calculation method of the pattern can be adjusted and improved according to the specific application and algorithm. Of course, there are other methods, such as methods based on local structure tensors, oriented gradient histograms, etc., which can also be used to calculate the orientation map of fingerprints.

Bounding Box

The bounding box is used to locate and surround the fingerprint area in the fingerprint image. The bounding box is represented as a rectangular box whose position is determined by the coordinates (x, y) of the upper left corner and the width (w) and height (h).

1. Feature extraction:

a. Apply texture feature extraction algorithms to fingerprint images, such as Local Binary Patterns (LBP) or Histogram of Oriented Gradients (HOG), etc.
b. These texture feature algorithms can extract texture information in images and capture the local ridge patterns and directions of fingerprints.

2. Binarization processing

a. 将提取的纹理特征进行二值化处理，将其转换为二值图像，使得指纹纹线和纹理特征更明显可见。

3. Edge detection

a. 对二值化的纹理特征图像进行边缘检测，可以使用Canny边缘检测算法或其他边缘检测算法。
b. 边缘检测可以帮助提取指纹的边界轮廓。

4.Contour extraction

a. 从边缘图像中提取指纹区域的轮廓，可以使用轮廓提取算法，如找到连通区域或应用形态学操作（如腐蚀和膨胀）。

5. Bounding box calculation

a. 根据提取的指纹区域轮廓，计算边界框的位置和大小。
b. 边界框的位置由左上角的坐标（x，y）确定，宽度（w）和高度（h）可以根据轮廓的最小包围矩形或最小外接矩形计算得到。

Python

def calculate_bounding_box(image):
    # 对指纹图像应用纹理特征提取算法，例如LBP或HOG
    # 这里仅作示例，可以根据实际需求选择适合的特征提取方法
    # 这里假设已经得到了提取的纹理特征图像

    # 进行二值化处理
    ret, binary_image = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

    # 进行轮廓提取
    contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # 计算边界框
    x, y, w, h = cv2.boundingRect(contours[0])  # 这里仅考虑第一个轮廓

    return x, y, w, h

# 读取指纹图像
fingerprint_image = cv2.imread('fingerprint.png', cv2.IMREAD_GRAYSCALE)

# 计算边界框
x, y, w, h = calculate_bounding_box(fingerprint_image)

# 绘制边界框
bounding_box_image = cv2.cvtColor(fingerprint_image, cv2.COLOR_GRAY2BGR)
cv2.rectangle(bounding_box_image, (x, y), (x + w, y + h), (0, 255, 0), 2)

C++

cv::Rect calculateBoundingBox(const cv::Mat& image) {
    
    
    cv::Mat binaryImage;
    
    // 对指纹图像应用纹理特征提取算法，例如LBP或HOG
    // 这里仅作示例，可以根据实际需求选择适合的特征提取方法
    // 这里假设已经得到了提取的纹理特征图像
    
    // 进行二值化处理
    cv::threshold(image, binaryImage, 0, 255, cv::THRESH_BINARY | cv::THRESH_OTSU);

    // 进行轮廓提取
    std::vector<std::vector<cv::Point>> contours;
    cv::findContours(binaryImage, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);

    // 计算边界框
    cv::Rect boundingBox = cv::boundingRect(contours[0]);  // 这里仅考虑第一个轮廓

    return boundingBox;
}

First, it loads the fingerprint image and uses the ORB feature detector to detect keypoints and compute feature descriptors. It then visualizes the keypoints and computes the bounding box of the fingerprint. Next, the direction of the fingerprint is estimated using the SIFT algorithm and an orientation map is drawn. Finally, the fingerprint image with keypoints, bounding boxes, and orientation maps is displayed.

In fact, position and pose estimation in fingerprint recognition systems usually require more complex algorithms and techniques, such as image registration and feature matching. Specific implementation methods and algorithm choices will vary based on specific applications and requirements.