【正文】 3 漏檢1 虛警2 光線昏暗且有遮擋的兩張人臉 檢出2 漏檢0 虛警17 結(jié)論與對(duì)未來的展望通過對(duì)上述測(cè)試結(jié)果的分析，我們得知一個(gè)完備的人臉檢測(cè)系統(tǒng)是很難被設(shè)計(jì)出來的，特別是將單一系統(tǒng)應(yīng)用于不同領(lǐng)域內(nèi)的人量檢測(cè)。皮膚的顏色、人臉的非剛性特點(diǎn)、性別、年齡、是否配戴眼鏡以及光照環(huán)境都可能影響人臉檢測(cè)的結(jié)果。在本文中，我們對(duì)AdaBoost算法進(jìn)行了綜合的闡述，并對(duì)haar特征進(jìn)行了擴(kuò)展，使其更接近于一個(gè)完備的特征集。同時(shí)我們對(duì)弱分類器的構(gòu)造過程也提出了自己的建議，將原本的一個(gè)適應(yīng)性閾值調(diào)整為上下限兩個(gè)閾值。當(dāng)然可以擴(kuò)展的地方還有很多，比如近幾年來有很多前輩在AdaBoost算法本身和強(qiáng)分類器級(jí)聯(lián)結(jié)構(gòu)方面的改進(jìn)也做出了很大的貢獻(xiàn)。在文章的最后我們?cè)敿?xì)介紹了基于AdaBoost算法在OpenCV中的相關(guān)應(yīng)用及測(cè)試結(jié)果。在這里我們對(duì)未來的人臉檢測(cè)工作做出一些展望：我們可以嘗試引進(jìn)更多類型的特征，如三角特征，并將三角特征與矩形特征結(jié)合使用。當(dāng)然，對(duì)于弱分類器的閾值設(shè)定，我們應(yīng)該不僅僅只考慮正面樣本，而且也要考慮負(fù)面樣本。同時(shí)，對(duì)于不同人臉檢測(cè)方法的融合方面，我們也有很多工作可以做，畢竟對(duì)于不同領(lǐng)域，每一人臉檢測(cè)算法都有其長(zhǎng)足之處。當(dāng)然我們也可以將語音、指紋檢測(cè)等應(yīng)用成熟的方法引用到人臉檢測(cè)系統(tǒng)中來。 參考文獻(xiàn)[1] Belhumeur ., Hespanha ., Kriegman . 1997. 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Digital Image Process(Second Edition).Prentice Hall. 460~514[21] 張兆禮，趙春暉，梅曉丹. 2001. 現(xiàn)代圖像處理技術(shù)及Matlab實(shí)現(xiàn). 北京：人民郵電出版社. 83~142 英文原文2010 Second International Conference on Computer Modeling and SimulationAdaBoostbased Face Detection in Color Images with Low False AlarmSania Arjomand InalouDepartment of Electrical EngineeringInternational Sharif University of TechnologyKish Island, Iran@Shohreh KasaeiDepartment of Computer EngineeringSharif University of TechnologyTehran, Iranskasaei@AbstractIn this paper, we have proposed a new face detection method which bines the AdaBoost algorithm with skin color information and support vector machine (SVM). First, a cascade classifier based on AdaBoost is used to detect faces in images. Due to noise and illumination changes some nonfaces might be detected too, therefore we have used a skin color model in the YCbCr color space to remove some of the detected nonfaces. Finally, we have utilized SVM to detect faces more accurately. Experimental results show that the performance of the proposed method is higher than the basic AdaBoost in the sense of detecting fewer nonfaces.Keywordsface detection。 AdaBoost。 skin color features。 Support Vector Machine (SVM)I. INTRODUCTIONFace detection has received much more attention in recent years. It is the first step in many applications such as face recognition, facial expression analysis, surveillance, security systems and human puter interface (HCI). Therefore, the performance of these systems depends on the efficiency of face detection process. The main aim in face detection is to determine the location of probable faces in images. Face detection according to various approaches, are classified into four categories. (i) knowledgebased, (ii) template matching, (iii) featurebased, and (iv) machine learning methods.Knowledgebased methods detect faces based on some roles which capture the relationships among facial features. Template matching methods find the similarity between input image and the template. Featurebased methods use some features (such as color, shape, and texture) to 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