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代写论文:人脸识别的结构
人脸识别是一项极具挑战性的技术,在这一领域有着非常重要的研究课题计算机科学,视觉和模式识别技术。这是因为人脸识别可以

受到姿势、光照和面部表情的影响。有许多新兴的应用程序从执法到商业需求,从娱乐到社交媒体。

人脸识别是行业最重要的需求(Wang et al., 2014)。自动化和有效的人脸识别是当今世界的主要需求。有显著的对人脸识别的研究。一些研究人员已经致力于解决这些问题,但人脸识别技术中有许多挑战需要解决。在人脸识别中最重要的问题是姿势的改变,面部表情的改变,场景和方位的照明(Wang et al., 2014)。人们已经注意到,当大小面对数据库的增加,人脸识别时间成为一个大问题

代写论文:人脸识别的结构
人脸识别或人脸表示在各种情况下被显著地使用为non生物识别技术联系。传统的人脸识别方法不能满足当前的要求。

市场(Kasar et al., 2016)。这是因为传统的方法产生的精确度很低,它甚至在不同的场合都受到限制。不受限制的人脸识别需要大量的努力和技术。然而,目前的人脸识别技术已经成功地产生了高分辨率的图像精度和低侵入性。人脸识别产生了“生理”的准确性(Kasar等,2016,第83页)。这就是那张脸的原因认知引起了心理学、安全、犯罪等领域研究者的关注研究,图像处理和计算机视觉。人脸识别是在各种算法和在多媒体图像处理中具有重要的意义

代写论文:人脸识别的结构
Face recognition is a very challenging technology that has very significant topic of research in the area ofcomputer science, vision and pattern recognition techniques. This is because face recognition can be affected by the pose, illumination and facial expression. There are many emerging applications and requirements from law enforcement to commercial requirement and from entertainment to social media.
Face recognition is the most important demand of the industry (Wang et al., 2014). The automated and
efficient face recognition is the major demand in the contemporary world. There has been significant
research done on the face recognition. Several researchers have worked to solve the problems, yet there are many challenges in the face recognition technology that are required to be resolved. Some of the significant issues that are aced during face recognition are change of the pose, expressions of face,illumination of the scene and orientation (Wang et al., 2014). It has been noticed that when size of the face database increases, the face recognition time becomes a big problem.

代写论文:人脸识别的结构
Face recognition or the face representation is significantly used in various circumstances as the non
contact biometrics. The conventional method of face recognition could not justify the current demands of the market (Kasar et al., 2016). This is because the conventional method produced very low accuracy and it was even restricted at various occasions. The unrestricted face recognition requires significant efforts and techniques. However, the current technology in face recognition is successful in producing high accuracy and low intrusiveness. Face recognition has produced the accuracy of the “physiological approach without being intrusive” (Kasar et al, 2016, p. 83). This has been the reason that face recognition has drawn the attention of the researchers from the field of psychology, security, criminal studies, image processing and computer vision. Face recognition is produced in various algorithms and has proved to be very important in the multimedia image processing.