近年来，人脸识别已是人工智能浪潮中最火热的技术之一，正大规模走入各行各业，既方便了人们的生活，又能提高效率，逐渐成为日常生活中的“标配”。随着人脸识别技术的成熟，现今的人脸识别算法识别率、识别速度、误判率等各项指标的优异性日益显现，人脸技术的进步将为更好的满足大众客户需求。
In recent years, face recognition has become one of the hottest technologies in the wave of artificial intelligence. It is entering all walks of life on a large scale, which not only facilitates people's life, but also improves efficiency, and gradually becomes the "standard" in daily life. With the maturity of face recognition technology, the recognition rate, recognition speed, error rate and other indicators of face recognition algorithm are becoming more and more outstanding. The progress of face technology will better meet the needs of the public customers.
人脸识别，是基于人的脸部特征信息进行身份识别的一种生物识别技术。用摄像机或摄像头采集含有人脸的图像或视频流，并自动在图像中检测和跟踪人脸，进而对检测到的人脸进行脸部识别的一系列相关技术，通常也叫做人像识别、面部识别。Face recognition is a kind of biometric technology based on the facial feature information. Using camera or camera to collect the image or video stream containing face, and automatically detect and track the face in the image, and then carry out face recognition for the detected face, a series of related technologies, usually also called human image recognition and face recognition.
人脸自动对焦和笑脸快门技术：首先是面部捕捉。它根据人的头部的部位进行判定，首先确定头部，然后判断眼睛和嘴巴等头部特征，通过特征库的比对，确认是人面部，完成面部捕捉。然后以人脸为焦点进行自动对焦，可以大大的提升拍出照片的清晰度。 笑脸快门技术就是在人脸识别的基础上，完成了面部捕捉，然后开始判断嘴的上弯程度和眼的下弯程度，来判断是不是笑了。以上所有的捕捉和比较都是在对比特征库的情况下完成的，所以特征库是基础，里面有各种典型的面部和笑脸特征数据。Face auto focus and smiling face shutter Technology: first, face capture. It judges the head according to the position of the head, first determines the head, then judges the head features such as eyes and mouth, through the comparison of the feature library, confirms that it is the human face, and completes the face capture. Then focus on the face automatically, which can greatly improve the definition of the photos. Smiling face shutter technology is based on face recognition, complete the face capture, and then start to judge the mouth bending degree and the eye bending degree, to judge whether smiling or not. All the above capture and comparison are done in the case of comparative feature library, so the feature library is the basis, which contains a variety of typical facial and smiley face feature data.
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