发帖
雷达卡
摘要翻译：
在生物识别领域，基于虹膜、指纹或掌纹扫描等的识别系统通常被认为更可靠，因为这些实体的属性随时间的变化非常小。然而，在过去的十年里，计算机的数据处理能力得到了极大的提高，这使得实时视频内容分析成为可能。这说明小时的需要是一种鲁棒性强、自动化程度高、准确率可信的人脸检测识别算法。本文提出的基于离散小波变换(DWT)的人脸检测与识别系统从包含图像质量较差的低成本设备如VGA摄像机、网络摄像头甚至闭路电视的图像的数据库中接收人脸帧作为输入。然后利用L*a*b*颜色空间的性质检测人脸区域，只提取正面人脸，从而消除所有附加背景。此外，该提取的图像被转换为灰度，其尺寸被调整为128×128像素。然后将小波变换应用于整个图像以获得系数。通过将属于测试图像的DWT系数与配准的参考图像的DWT系数进行比较来进行识别。在此基础上，运用欧氏距离分类器对数据库中的测试图像进行了验证。得到了不同层次DWT分解的精度，并对其进行了比较。
---
英文标题：
《A robust, low-cost approach to Face Detection and Face Recognition》
---
作者：
Divya Jyoti, Aman Chadha, Pallavi Vaidya, and M. Mani Roja
---
最新提交年份：
2011
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Cryptography and Security 密码学与安全
分类描述：Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
涵盖密码学和安全的所有领域，包括认证、公钥密码系统、携带证明的代码等。大致包括ACM主题课程D.4.6和E.3中的材料。
--
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--
---
英文摘要：
In the domain of Biometrics, recognition systems based on iris, fingerprint or palm print scans etc. are often considered more dependable due to extremely low variance in the properties of these entities with respect to time. However, over the last decade data processing capability of computers has increased manifold, which has made real-time video content analysis possible. This shows that the need of the hour is a robust and highly automated Face Detection and Recognition algorithm with credible accuracy rate. The proposed Face Detection and Recognition system using Discrete Wavelet Transform (DWT) accepts face frames as input from a database containing images from low cost devices such as VGA cameras, webcams or even CCTV's, where image quality is inferior. Face region is then detected using properties of L*a*b* color space and only Frontal Face is extracted such that all additional background is eliminated. Further, this extracted image is converted to grayscale and its dimensions are resized to 128 x 128 pixels. DWT is then applied to entire image to obtain the coefficients. Recognition is carried out by comparison of the DWT coefficients belonging to the test image with those of the registered reference image. On comparison, Euclidean distance classifier is deployed to validate the test image from the database. Accuracy for various levels of DWT Decomposition is obtained and hence, compared.
---
PDF链接：
https://arxiv.org/pdf/1111.109
京公网安备 11010802022788号
