Biometrics 简明教程

Biometrics and Image Processing

在信息时代，图像占据了很大份额。在生物特征识别中，需要图像处理才能识别在数据库中存储的生物特征图像的个人。面部，指纹，虹膜等是基于图像的生物特征识别，需要图像处理和模式识别技术。

Images have a huge share in this era of information. In biometrics, image processing is required for identifying an individual whose biometric image is stored in the database previously. Faces, fingerprints, irises, etc., are image-based biometrics, which require image processing and pattern recognition techniques.

为了使基于图像的生物识别系统准确工作，它需要以非常清晰且未经篡改的形式获得用户生物特征的样本图像。

For an image based biometric system to work accurately, it needs to have the sample image of user’s biometric in a very clear and non-adulterated form.

Requirement of Image Processing in Biometrics

用户生物特征的图像被输入到生物识别系统中。该系统被编程为使用方程式来处理图像，然后存储每个像素的计算结果。

The image of user’s biometric is fed into the biometric system. The system is programmed to manipulate the image using equations, and then store the results of the computation for each pixel.

为了选择性地增强数据中的某些精细特征并消除某些噪声，数字数据要经过各种图像处理操作。

To selectively enhance certain fine features in the data and to remove certain noise, the digital data is subjected to various image processing operations.

图像处理方法可以分为三类功能 −

Image processing methods can be grouped into three functional categories −

Image Restoration

图像复原主要包括 −

Image restoration mainly includes −

  1. Reducing noise introduced in the image at the time of acquiring sample.

  2. Removing distortions appeared during enrollment of biometric.

图像平滑可以降低图像中的噪声。平滑过程是通过将每个像素替换为与相邻像素的平均值来进行的。生物识别系统使用各种滤波算法和降噪技术，例如中值滤波、自适应滤波、统计直方图、小波变换等。

Image smoothing reduces noise in the image. Smoothing is carried out by replacing each pixel by the average value with the neighboring pixel. The biometric system uses various filtering algorithms and noise reduction techniques such as Median Filtering, Adaptive Filtering, Statistical Histogram, Wavelet Transforms, etc.

Image Enhancement

图像增强技术可以改善图像的任何部分或特征的可见性并抑制其他部分的信息。它仅在还原完成后才会进行操作。它包括提高亮度、锐化、调整对比度等，以便图像可用于进一步处理。

Image enhancement techniques improve the visibility of any portion or feature of the image and suppress the information in other parts. It is done only after restoration is completed. It includes brightening, sharpening, adjusting contrast, etc., so that the image is usable for further processing.

Feature Extraction

从图像中可以提取两种类型的特征，即：

Two types of features are extracted from image, namely −

  1. General features − The features such as shape, texture, color, etc., which are used to describe content of the image.

  2. Domain-specific features − They are application dependent features such as face, iris, fingerprint, etc. Gabor filters are used to extract features.

feature extraction

当从图像中提取特征时，你需要选择一个合适的分类器。广泛使用的分类器 Nearest Neighbor classifier ，它将候选图像的特征矢量与存储在数据库中的图像矢量进行比较。

When the features are extracted from the image, you need to choose a suitable classifier. The widely used classifier Nearest Neighbor classifier, which compares the feature vector of the candidate image with the vector of the image stored in the database.

B-Splines 是用于描述指纹生物识别系统中曲线模式的近似值。B 样条的系数用作特征。在虹膜识别系统的情况下，虹膜图像使用离散小波变换 (DWT) 分解，然后 DWT 系数用作特征。

B-Splines are approximations applied to describe curve patterns in fingerprint biometric systems. The coefficients of B-Splines are used as features. In case of iris recognition system, the images of iris are decomposed using Discrete Wavelet Transform (DWT) and the DWT coefficients are then used as features.