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外文原文1
外文翻译14
TEMPLATE MATCHING FOR DETECTION amp; RECOGNITION OF FRONTAL VIEW OF HUMAN FACE THROUGH MATLAB
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Namrata Singh Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur namrata.singh258@gmail.com |
A. K. Daniel Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur danielak@rediffmail.com |
Pooja Chaturvedi Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur chaturvedi.pooja03@gmail.com |
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 amp; 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 amp; 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) amp; chrominance (Cr), Color segmentation, skintone statistics amp; eye-mouth region computation. A template matching algorithm using cross correlation method for locating amp; 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 amp; Face recognition. The performance of given approach has been evaluated on the basis of run time amp; accuracy. The simulation result shows that the defined model is efficient in terms of accuracy which is 81% and the false alarms are reduced.
Keywords— Face detection, recognition, template matching, Cross correlation method, energy distribution.
Ⅰ.INTRODUCTION
Image processing is a method to convert a real-time image into digital form amp; perform analysis amp; computation on it in order to get its improved quality. This method is of two aspects: First aspect is to improve the visual appearance of images to a human viewer amp; the second aspect is to prepare images for measurement of features amp; structures present in that image. In image processing, face detection amp; recognition system is a physical characteristics recognition technology using the inherent physiological features of human faces for identification of a human being. Face detection scans images, apply filtering procedures, use feature extraction techniques to represent face candidates in an image. Face recognition is used for identifying a candidate from the set of detected face candidate by matching procedure. The face detection amp; recognition technology need not to be carried out and will not be lost, so it is convenient and safe for use. It seems very easy to detect amp; recognize face images but in reality, we have to consider various constraints like intensity, shape, size, color, texture, bounding rectangles, pose, age etc. So, various false alarms may also be detected in an image. These false alarms reduce the accuracy of face detection algorithms. The computation of face detection amp; recognition models is fascinating because they can contribute to theoretical learning as well as practical applications. Face detection amp; recognition algorithms are used in various applications such as security management, criminal identification, access control, law enforcement, biometric signal analysis, surveillance amp; Human-computer interaction etc. Various face detection algorithms exist based on finding faces in images with controlled background, by shape using bounding rectangles, by color, motion etc. In this paper, the algorithms used for face detection are based on two of them i.e. finding faces based on color amp; shape using bounding rectangle for better accuracy and reduced false detection. Also, template matching algorithm with cross correlation method has been used for recognition of accurate face candidate. Various objectives of the technique described here are to develop a system to detect amp; recognize only expressionless, frontal view of faces in an image, to represent a fast algorithm that automatically detect face regions, to represent a system with high degree of variance amp; reduced false alarms of detected image. Thus, the aim of this paper is to represent a fast, robust, reasonably simple, accurate amp; easy to understand algorithms amp; techniques. Basic concept behind whole procedure is represented through the figure 1.
Figure 1. Overview of Basic Procedures
The organization of the paper is as follows: related work in the field of face detection and recognition and the various methodologies of detection and recognition used in determining the target face candidate is discussed in section 2, proosed methodology in section 3, hardware and software specifications in section 4, performance analysis in section 5, limitations in section 6 and section 7 concludes the paper with the conclusion and the future scope.
Ⅱ.RELATED WORK
This section describes the theories of face detection amp; recognition, various approaches of face detection amp; recognition and work done in the literature.
A. FACE DETECTION:
Face detection decides whethe
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