Principle analysis of face recognition technology based on face recognition lock

Principle analysis of face recognition technology based on face recognition lock

人脸识别锁人脸识别技术原理分析

Face recognition is mainly divided into three processes: face detection, feature extraction and face recognition.

Face detection: face detection is to detect and extract face image from input image. Generally, Haar feature and AdaBoost algorithm are used to train cascade classifier to classify each image. If a rectangular region passes through a cascade classifier, it is identified as a face image.

特征提取：特征提取是指通过一些数字来表征人脸信息，这些数字就是我们要提取的特征。常见的人脸特征分为两类，一类是几何特征，另一类是表征特征。几何特征是指眼睛、鼻子和嘴等面部特征之间的几何关系，如距离、面积和角度等。由于算法利用了一些直观的特征，计算量小。不过，由于其所需的特征点不能精确选择，限制了它的应用范围。另外，当光照变化、人脸有外物遮挡、面部表情变化时，特征变化较大。所以说，这类算法只适合于人脸图像的粗略识别，无法在实际中应用。

Feature extraction: feature extraction refers to the representation of face information by some numbers, which are the features we want to extract. The common face features are divided into two categories, one is geometric features, the other is characterization features. Geometric features refer to the geometric relationships among facial features such as eyes, nose and mouth, such as distance, area and angle. Because the algorithm uses some intuitive features, the amount of calculation is small. However, its application is limited because the required feature points can not be selected accurately. In addition, when the illumination changes, the face is covered by foreign objects, and the facial expression changes, the features change greatly. Therefore, this kind of algorithm is only suitable for rough recognition of face image, and can not be applied in practice.

　　特征提取：特征提取是指通过一些数字来表征人脸信息，这些数字就是我们要提取的特征。常见的人脸特征分为两类，一类是几何特征，另一类是表征特征。几何特征是指眼睛、鼻子和嘴等面部特征之间的几何关系，如距离、面积和角度等。由于算法利用了一些直观的特征，计算量小。不过，由于其所需的特征点不能精确选择，限制了它的应用范围。另外，当光照变化、人脸有外物遮挡、面部表情变化时，特征变化较大。所以说，这类算法只适合于人脸图像的粗略识别，无法在实际中应用。

Representation features use the gray information of face image to extract global or local features through some algorithms. LBP is one of the most commonly used feature extraction algorithms. LBP method first divides the image into several regions, and uses the center value as the thresholding value in the neighborhood of 640x960 pixels in each region. The result is regarded as a binary number.

　　表征特征利用人脸图像的灰度信息，通过一些算法提取全局或局部特征。其中比较常用的特征提取算法是LBP算法。LBP方法首先将图像分成若干区域，在每个区域的像素640x960邻域中用中心值作阈值化，将结果看成是二进制数。

Face recognition: the face recognition mentioned here is a narrow sense of face recognition, that is, the extracted features of the face to be recognized are compared with the features of the face in the database, and the classification is determined according to the similarity. And face recognition can be divided into two categories: one is confirmation, which is the process of comparing the face image with the person's image stored in the database to answer your question or not; The other is recognition, which is the process of matching a face image with all the images stored in the database to answer the question of who you are. Obviously, face recognition is more difficult than face recognition because it needs massive data matching. The commonly used classifiers are nearest neighbor classifier, support vector machine and so on.

　　人脸识别：这里提到的人脸识别是狭义的人脸识别，即将待识别人脸所提取的特征与数据库中人脸的特征进行对比，根据相似度判别分类。而人脸识别又可以分为两个大类：一类是确认，这是人脸图像与数据库中已存的该人图像比对的过程，回答你是不是你的问题;另一类是辨认，这是人脸图像与数据库中已存的所有图像匹配的过程，回答你是谁的问题。显然，人脸辨认要比人脸确认困难，因为辨认需要进行海量数据的匹配。常用的分类器有最近邻分类器、支持向量机等。

Similar to fingerprint application, intelligent door lock is a mature technology of face recognition. Because in the face recognition intelligent door lock system, the user is active cooperation, can obtain the qualified face in a specific environment. This provides a good input source for face recognition, and can often get satisfactory results.

　　与指纹应用方式类似，人脸识别技术目前比较成熟的也是智能门锁。因为在人脸识别智能门锁系统中，用户是主动配合的，可以在特定的环境下获取符合要求的人脸。这就为人脸识别提供了良好的输入源，往往可以得到满意的结果。