【正文】 extract facial features to obtain face locations. Machine learning methods use techniques from statistical analysis and machine learning to find the relevant characteristics of faces and nonfaces. Despite of the notable successes achieved in past decades, making a tradeoff between putational plexity and detection efficiency is still the main challenge.This paper proposes a method for color face detection using AdaBoost algorithm bined with skin color information and support vector machine (SVM). The rest of this paper is organized as follows. In Section 2, related work is explained. Proposed face detection algorithm is described in Section 3. Experimental results are presented in Section 4, and finally Section 5 concludes the paper.II. RELATED WORKA. Face Detection Using AdaBoostViola and Jones proposed a totally corrective face detection algorithm in. They used a set of Haarlike features to construct a classifier. Every weak classifier had a simple threshold on one of the extracted features. AdaBoost classifier was then used to choose a small number of important features and bines them in a cascade structure to decide whether an image is a face or a nonface. 1) Haarlike FeaturesA set of Haarlike features used as the input features to the cascade classifier, are shown in Fig. 1. Computation of Haarlike features can be accelerated using an intermediate image representation called the integral image. An integral image was defined as the sum of all pixel values (in an image) above and to the left, including itself. 2) AdaBoost LearningAdaBoost is an algorithm for constructing a posite classifier by sequentially training classifiers while putting more and more emphasis on certain patterns. It can be summarized as follows: a) Consider example images ,…, where stand for negative and positive examples, respectively.Figure 1. Example of Haarlike features b) Initialize Weights (1)Where m and n are the number of positive and negative examples, respectively, and L = m + n. c) Do for t=1, … ,T:1. Normalize the weights （2）2. For each feature, j, train a classifier , and calculate its error with respect to as （3）3. Choose the classifier with lowest error 4. Update the weights: （4） d) Final classifier is: （5）where 3) Detection CascadeIn order to greatly improve the putational efficiency and to also reduce the false positive rate, a sequence of increasingly more plex classifiers called a cascade is built. Fig. 2 shows the cascade.All SubWindows132Further processingTTTFFFReject SubWindowsFigure 2. Schematic depiction of a detection cascade.Every stage of the cascade either rejects the analyzed window or passes it to the next stage. Only the last stage may finally accept the window. So, to be accepted, a window must pass through the whole cascade, but rejection may happen at any stage. During detection, most subwindows of the analyzed image are very easy to reject, so they are rejected at early stage and do not have to pass the whole cascade. Stages in cascade are constructed by training classifiers using AdaBoost.B. Skin Color DetectionColor is a powerful fundamental cue of human faces. Distribution of skin color clusters lay on a small region of the chromatic color space. Skin color can be used as plementary information to other features (such as shape and geometry) and can be used to build more accurate face detection methods. The primary step for skin color detection in an image is to choose a suitable color space. There are many color spaces。 such as RGB, HSV, YCbCr, YUV, and CIELab. However, it has always been a disputable issue that which color space is the best for describing the property of human skin. The next step of skin color detection is to build a decision rule that discriminates between skin and nonskin pixels. This is usually acplished by the skin color modeling method. Current approaches can be classified into two main classes. (i) parametric methods (such as the single Gaussian, and mixtures of Gaussians), and (ii) nonparametric methods (such as Bayes classifier and self organizing map (SOM)). Furthermore some new approaches are based on learning (such as cellular learning automata (CLA)).C. Support Vector MachineSupport vector classifiers implicitly map the data into a high dimensional feature space via a nonlinear transform and pute a hyperplane which separates the data in the feature space by a large margin. Intuitively, a good choice is a hyperplane that leaves the maximum margin between the two classes (–1, +1) and minimizes a quantity proportional to the number of misclassification errors. Its dual quadratic programming classification problem is: （6）Where are the Lagrange multipliers, and C is a constant variable that controls the tradeoff between misclassification error and the margin.The functional form of the mappingdoes not need to be known since it is defined by the choice of kernel （7）The vector w has the form of and therefore （8）The training examples with are called support vectors.III. PROPOSED FACE DETECTION METHODIn this paper, we have proposed a face detection method for color images using AdaBoost algorithm bined with skin color information and SVM. Fig. 3 shows the overall structure of the proposed method.In the first stage, like Viola and Jones, we have used a cascade classifier which is constructed by AdaBoost algorithm to detect faces. As we mentioned above, there are three main contributions of the AdaBoostbased face detection. The first is the integral image which allows the Haarlike features to be puted very rapidly. The second is the AdaBoost algorithm which selects a small number of Haarlike features from a larger set and creates efficient classifiers. The third contribution is a method for bining classifiers in a cascade in order to improve the putational efficiency and to also reduce the false positi