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A Real-time Face Detection And Recognition System
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A Real-time Face Detection And Recognition System Document Transcript

  • 1. A Real-time Face Detection And Recognition System Keqing Shi1 2 3, Shurong Pang1 2 3, Fengqi Yu1 2 1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China 2 The Chinese University of Hong Kong, Hong Kong, China 3 Graduate University of Chinese Academy of Sciences, Beijing, China kq.shi@siat.ac.cn,sr.pang@siat.ac.cn, fq.yu@siat.ac.cnAbstract—This paper describes a face detection framework that Some face detection applications in video can be found inis capable of processing image or video fast while achieving high references [15, 16].detection rate. There are three key constructions. First, thedatabase of MIT is adopted and image examples are used to train The development of cheap, high quality video cameras hasa linear classifier, which is based on the Haar-like features. generated new interests in extending still image-basedSecondly, in order to make the system efficiently we utilize recognition methodologies to video sequences. In recent years,Principal Component Analysis (PCA). Thirdly, considering the research on this area has attracted great interests from scientistshigh false detection rate when only use Haar-like features, we and researchers worldwide.take the edge contour detection as a compensation. Because faceis a part of human body, a face-like image which does not belong Traditional face detection and recognition system canto a human body can be detected using our technique. detect and recognize face from image or video. But it canExperiment results show that our method has a low false easily make false detection in a face-like object which is part ofdetection rate. human body. To solve this problem, we add edge contour detection which detects the human body. Keyword-face detection; Haar-like feature;PCA; edge contour The rest of the paper is organized as follows. Section II I. INTRODUCTION shows the scheme of the system and describe the algorithm we use. Section III shows the results of our experiment. Section IV Face detection and recognition have become an interesting show the conclusion.research topic due to their enormously commercial and lawenforcement applications. The research on the video-based II. THE PROPOSED FACE DECTION AND RECOGNITIONface detection and recognition can be considered as the SCHEMEcontinuation and extension of the still-image face recognition, The flowchart of the proposed face detection and facewhich has been extensively researched for years and somegood results have been reported in the literatures. For example, recognition system is shown in Fig.1.the well-known methods such as Principal ComponentAnalysis (PCA) [1], Linear Discriminant Analysis (LDA) [1],Handsdorff distance measure for face recognition [2], ElasticGraph Matching (EGM) [3], eigenspace-based facerecognition [4], a novel hybrid neural and dual eigenspacesmethods for face recognition [5], eigenfaces and Fisherfacesmethods [6]. In order to capture the frontal face imageaccurately and timely, many face detection methods have beenproposed, such as face detection in color images based on thefuzzy theory [7], the discriminating feature analysis andSupport Vector Machine (SVM) classifier for face detection[8], neural network-based face detection [9]. Face colorinformation is an important feature in the face detection. Inreference [12], a latest survey of skin-color modeling anddetection methods was presented. Statistical color moduleswith application to skin detection was reported in reference[11]. The quantized skin color regions for face detection weregiven in reference [10]. Eye is another important feature forface detection and recognition. For example, a robust methodfor eye feature extraction on the color image was reported inreference [13]. Using optimal Wavelet packets and radial basisfunctions for eye detection was introduced in reference [14]. Figure 1. Face detection and recognition schematic flowchart. 978-1-4577-1415-3/12/$26.00 ©2012 IEEE 3074
  • 2. 2.1 PCA Algorithm classifier. The classifier is designed so that it can be easily The process of PCA face detection is as follows. Suppose "resized" in order to be able to find the objects of interest atthere are N pieces of face images, i.e. training sample set with different sizes, which is more efficient than resizing the imageN images. The size of each image is K*L. They form a vector itself. So, to find an object of an unknown size in the image theset { x1 ,x2 ,... xN }, Rn is a N-dimensional Space and each vector scan procedure should be done several times at different scales.is in the space. The total population scatter matrix is defined as: N 1 1 ST ( xk )( x k )T XX T N k 1 Nwhere xk is the training sample, Rn, n is the average vectorof all samples, X=[x1 - , x2 - ,... xn - ]. Structure matrixR= XTX . it is very easy to obtain the eigenvalue of R. Sortingeigenvalues in descending order ... . Itscorresponding orthogonal normalized eigenvector is vi (i =1,2, ...,N) . The orthogonal normalized eigenvector ei of ST canbe obtained as: 1 ei X i i 1,2,...N i Figure 2. Haar-like features. Select the pre-M eigenvectors to build the feature subspace 2.3 Edge contouE = {e1, e2, ... eM}, Human face image projection to the Because face is a part of human body, something, like facesubspace will get a set of coordinate coefficients. X is the sub but does not along to human body, is not real face. To detectwindow which is projection to feature subspace. When judged human body in images or from video, we use the Histograms ofwhether it exist human face in target sub window, and get its Oriented Gradients (HOG) Algorithm.co-efficient vector is: y = ET(x- ), its reconstruction subwindow is: x = + Ey , the reconstruction signal-to-noise Histogram of Oriented Gradients (HOG) are featureratio is defined as: descriptors used in computer vision and image processing for the purpose of object detection. The technique counts 2 x occurrences of gradient orientation in localized portions of an r x 10 lg 2 image. This method is similar to that of edge orientation x x histograms, scale-invariant feature transform descriptors, and shape contexts, but differs in that it is computed on a dense grid Set a threshold T, if r(x) > T , then a human face in the sub of uniformly spaced cells and uses overlapping local contrastwindow exists. normalization for improved accuracy.2.2 Haar-like feature Navneet Dalal and Bill Triggs [23], researchers for the A recognition process can be much more efficient if it is French National Institute for Research in Computer Sciencebased on the detection of features that encode some and Control (INRIA), first described Histogram of Orientedinformation about the class to be detected. This is the case of Gradient descriptors in their June 2005 paper to the CVPR. InHaar-like features (shown in Fig. 2) that encode the existence this work they focused their algorithm on the problem ofof oriented contrasts between regions in the image. A set of pedestrian detection in static images, although since then theythese features can be used to encode the contrasts exhibited by expanded their tests to include human detection in film anda human face and their special relationships. Haar-like features video, as well as to a variety of common animals and vehiclesare so called because they are computed similar to the in static imagery.coefficients in Haar wavelet transforms. The essential thought behind the Histogram of Oriented The object detector of OpenCV has been initially proposed Gradient descriptors is that local object appearance and shapeby Paul Viola [21] and improved by Rainer Lienhart [22]. First, within an image can be described by the distribution ofa classifier (namely a cascade of boosted classifiers working intensity gradients or edge directions. The implementation ofwith Haar-like features) is trained with a few hundreds of these descriptors can be achieved by dividing the image intosample views of a particular object (e.g., a face or a car), called small connected regions, called cells, and for each cellpositive examples, that are scaled to the same size (say, 20x20), compiling a histogram of gradient directions or edgeand negative examples - arbitrary images of the same size. orientations for the pixels within the cell. The combination of these histograms then represents the descriptor. For improved After a classifier is trained, it can be applied to a region of accuracy, the local histograms can be contrast-normalized byinterest (of the same size as used during the training) in an calculating a measure of the intensity across a larger region ofinput image. The classifier outputs a "1" if the region is likely the image, called a block, and then using this value toto show the object (e.g., face/car), and "0" otherwise. To search normalize all cells within the block. This normalization resultsfor the object in the whole image one can move the search in better invariance to changes in illumination or shadowing.window across the image and check every location using the 3075
  • 3. The HOG descriptor maintains a few key advantages over 20 frames for one person using camera. Then our systemother descriptor methods. Since the HOG descriptor operates performs face recognition on image or camera. We use theon localized cells, the method upholds invariance to geometric confidence for the accuracy of the system. Figure 4 shows theand photometric transformations, except for object orientation. result of “skq”, its confidence is 94.0207%. Figure 5 and Fig.Such changes would only appear in larger spatial regions. 6 show two more examples of “lkd” and “Divid Beckham”Moreover, as Dalal and Triggs [23] discovered, coarse spatial respectively. The confidence of “lkd” is 91.8116%. When wesampling, fine orientation sampling, and strong local choose “David Beckham”, although the system recognizes itphotometric normalization permits the individual body as “lkd”, the confidence is only 1.6042%.movement of pedestrians to be ignored so long as theymaintain a roughly upright position. The HOG descriptor isthus particularly suited for human detection in images. 2.4 Datebase In order to train a classifier we need some positive sampleswhich have frontal faces only and negative samples containingnon-faces such as backgrounds. Table I and Fig. 3 show thedatabases for our samples. TABLE I. DATABASE OF FACE DETECTON. Database Description A total of 16 individuals faces, each person have 27 MIT images in a different light, different scales and different Fig. 4 face detection who named “skq” head angle. Collect a large of male and female faces and each image FERET contains a specific expression of the face. Contain 20 subjects, a total of 564 images, each subject UMIST have images from the side to the front. Yale Bespectacled face under different light conditions. AT&T Have 40 themes, each of 10 images. Harvard Cutted face images under different light conditions. Univ of 300 frontal face of the 30 individuals (10 per person) Bern and 150 of the side face (5 per person). MZVTS A different image sequence polymorphism database. 3276 facial expressions and obscured face images in Purdue AR different lighting conditions. Fig. 5 face detection who named “lkd” Figure 3. Samples used for training Haar-like feature classfier. Fig. 6 face detection who is “Divid Beckham” III. EXPERIMENT CONCLUSION Our system can detect and recognize human face in imageor video. Before detection and recognition, user needs to Our system can detect and recognize human face in imageenroll in the system, including name, sex, and age. We collect or video. Don’t like traditional face detection and recognition 3076
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