Template matching for detection & recognition of frontal view of human face through Matlab

Namrata Singh, A. K. Daniel, Pooja Chaturvedi
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引用次数: 6

Abstract

Human face is an important object in an image database due to its unique features (eyes, mouth, nose etc.) in every human being. The detection & recognition of a face in an image using template matching is one of the profound research interest in the field of image processing. Various approaches have been proposed in the literature to extract the visual facial features based on texture, color, shape, sketch & pose variance etc. for face detection in color images. This paper describes an approach of face detection technique that includes major characteristics such as lightening compensation based on luminance (Y) & chrominance (Cr), Color segmentation, skin-tone statistics & eye-mouth region computation. A template matching algorithm using cross correlation method for locating & recognizing a face has been applied on various face candidates to match the template with right face candidate. Thus, the presented work is divided into three steps: Face detection, Computation of template matching & Face recognition. The performance of given approach has been evaluated on the basis of run time & accuracy. The simulation result shows that the defined model is efficient in terms of accuracy which is 81% and the false alarms are reduced.
基于Matlab的人脸正面视图检测与识别模板匹配
人脸是图像数据库中的一个重要对象,因为每个人都具有独特的特征(眼睛、嘴巴、鼻子等)。利用模板匹配对图像中的人脸进行检测与识别是图像处理领域的研究热点之一。文献中提出了各种基于纹理、颜色、形状、素描和姿态方差等提取视觉面部特征的方法,用于彩色图像的人脸检测。本文介绍了一种基于亮度(Y)和色度(Cr)的亮度补偿、颜色分割、肤色统计和眼口区域计算等主要特征的人脸检测方法。将一种基于互相关方法的人脸定位识别模板匹配算法应用于各种候选人脸,将模板与正确的候选人脸进行匹配。因此,本文的工作分为三个步骤:人脸检测、模板匹配计算和人脸识别。从运行时间和精度两个方面对所提方法的性能进行了评价。仿真结果表明,该模型的准确率达到81%,减少了误报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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