基于圖像的人臉特征提取與發(fā)型分類
發(fā)布時(shí)間：2019-03-16 13:35
【摘要】：人臉特征提取是人臉圖像分析技術(shù)的關(guān)鍵,它被廣泛應(yīng)用于人臉識別、人臉表情分析、三維人臉重建等領(lǐng)域,然而人臉特征點(diǎn)定位不準(zhǔn)確的問題依然存在。頭發(fā)在人體表觀中具有重要作用,由于缺少有效的頭發(fā)分類技術(shù),大大降低了三維人臉變形、3D試衣的真實(shí)性。因此,本文針對上述問題進(jìn)行了相關(guān)研究,研究內(nèi)容主要包括:(1)人臉存在性及合理性的檢測。利用膚色檢測算法對經(jīng)過預(yù)處理后的圖像進(jìn)行處理,增強(qiáng)圖像中的人臉區(qū)域,通過協(xié)方差矩陣在人臉區(qū)域進(jìn)行坐標(biāo)系重建,根據(jù)其中心點(diǎn)位置構(gòu)建閾值關(guān)系,構(gòu)建人臉(包含脖頸)區(qū)域的最小外接包圍盒,根據(jù)閾值關(guān)系進(jìn)行人臉存在性及合理性的判定。(2)人臉特征提取。為了獲取測試圖像中的人臉特征數(shù)據(jù),使用主動形狀模型(ASM)算法進(jìn)行處理,針對原算法直接采用灰度值信息構(gòu)建局部輪廓模型,灰度值對外部自然因素比較敏感,本文采用特征點(diǎn)及其正負(fù)方向一定區(qū)域像素點(diǎn)的邊緣結(jié)構(gòu)方向構(gòu)建局部輪廓模型,該方法在特征點(diǎn)及相關(guān)像素點(diǎn)的法線方向上采集圖像的灰度分布特征,能有效利用圖像信息。在ASM搜索過程中,利用基于混合投影的眼睛精確定位方法實(shí)現(xiàn)眼睛區(qū)域的精確定位,根據(jù)眼睛位置信息確定模板形狀的平移、旋轉(zhuǎn)和尺度變化參數(shù)。然后,通過改進(jìn)的搜索策略進(jìn)行特征點(diǎn)匹配,進(jìn)一步提升特征點(diǎn)匹配的準(zhǔn)確度。(3)發(fā)型分類。提出一種基于顏色空間與曲線變化的能量模型實(shí)現(xiàn)對頭發(fā)區(qū)域的分割,根據(jù)輪廓檢測的圖像數(shù)據(jù),逐點(diǎn)對頭發(fā)邊緣進(jìn)行檢測分割。利用主成分分析(PCA)和支持向量機(jī)(SVM)方法實(shí)現(xiàn)對正面圖像的發(fā)型分類。同時(shí),通過輪廓曲線上各個(gè)像素點(diǎn)的曲率定義彎曲度,提出基于彎曲度的局部搜索算法實(shí)現(xiàn)對側(cè)面頭發(fā)長度的分類,然后,根據(jù)曲線曲率和經(jīng)驗(yàn)距離的局部區(qū)域搜索方法實(shí)現(xiàn)對扎辮子發(fā)型的分類。通過實(shí)驗(yàn),驗(yàn)證了以上方法的可行性,實(shí)驗(yàn)結(jié)果表明,改進(jìn)的ASM方法取得了較好的特征點(diǎn)定位結(jié)果,而本文的發(fā)型分類算法也得到了較好的發(fā)型分類結(jié)果。最后,本文分析了在人臉特征提取和發(fā)型分類方面存在的不足并提出了進(jìn)一步的研究方向。
[Abstract]:Face feature extraction is the key technology of face image analysis. It is widely used in face recognition, facial expression analysis, 3D face reconstruction and so on. However, the problem of inaccurate location of facial feature points still exists. Hair plays an important role in the appearance of human body. Due to the lack of effective hair classification technology, the 3D face deformation is greatly reduced, and the authenticity of 3D fitting is greatly reduced. Therefore, this paper studies the above problems, including: (1) the detection of the existence and rationality of face. The skin color detection algorithm is used to process the pre-processed image, enhance the face region in the image, reconstruct the coordinate system in the face region by covariance matrix, and construct the threshold relation according to the position of its center point. The minimal outer bounding box of the face (including neck) region is constructed to determine the existence and rationality of the face according to the threshold relation. (2) face feature extraction. In order to obtain the face feature data in the test image, the active shape model (ASM) algorithm is used to process it. Aiming at the original algorithm, the gray value information is directly used to construct the local contour model, and the gray value is sensitive to the external natural factors. In this paper, the local contour model is constructed by using the edge structure direction of the feature points and their positive and negative directions, and the gray distribution features of the images are collected in the normal direction of the feature points and related pixels, which can make use of the image information effectively. In the process of ASM search, the precise eye location method based on hybrid projection is used to locate the eye region accurately. According to the eye position information, the parameters of translation, rotation and scale change of the template shape are determined. Then, the accuracy of feature point matching is further improved by the improved search strategy. (3) hairstyle classification. An energy model based on color space and curve change is proposed to segment the hair region. According to the image data of contour detection, the edge of hair is detected and segmented point by point. Principal component analysis (PCA) and support vector machine (SVM) (SVM) are used to classify the hairstyle of positive images. At the same time, the curvature of each pixel on the contour curve is defined by curvature, and a local search algorithm based on curvature is proposed to realize the classification of the length of the side hair. According to the curvature of curves and empirical distance of the local region search method to achieve the classification of braided hair. The feasibility of the above method is verified by experiments. The experimental results show that the improved ASM method has achieved better feature point location results, and the hairstyle classification algorithm in this paper has also obtained better hairstyle classification results. Finally, the shortcomings of face feature extraction and hairstyle classification are analyzed and further research directions are proposed.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2441473
[Abstract]:Face feature extraction is the key technology of face image analysis. It is widely used in face recognition, facial expression analysis, 3D face reconstruction and so on. However, the problem of inaccurate location of facial feature points still exists. Hair plays an important role in the appearance of human body. Due to the lack of effective hair classification technology, the 3D face deformation is greatly reduced, and the authenticity of 3D fitting is greatly reduced. Therefore, this paper studies the above problems, including: (1) the detection of the existence and rationality of face. The skin color detection algorithm is used to process the pre-processed image, enhance the face region in the image, reconstruct the coordinate system in the face region by covariance matrix, and construct the threshold relation according to the position of its center point. The minimal outer bounding box of the face (including neck) region is constructed to determine the existence and rationality of the face according to the threshold relation. (2) face feature extraction. In order to obtain the face feature data in the test image, the active shape model (ASM) algorithm is used to process it. Aiming at the original algorithm, the gray value information is directly used to construct the local contour model, and the gray value is sensitive to the external natural factors. In this paper, the local contour model is constructed by using the edge structure direction of the feature points and their positive and negative directions, and the gray distribution features of the images are collected in the normal direction of the feature points and related pixels, which can make use of the image information effectively. In the process of ASM search, the precise eye location method based on hybrid projection is used to locate the eye region accurately. According to the eye position information, the parameters of translation, rotation and scale change of the template shape are determined. Then, the accuracy of feature point matching is further improved by the improved search strategy. (3) hairstyle classification. An energy model based on color space and curve change is proposed to segment the hair region. According to the image data of contour detection, the edge of hair is detected and segmented point by point. Principal component analysis (PCA) and support vector machine (SVM) (SVM) are used to classify the hairstyle of positive images. At the same time, the curvature of each pixel on the contour curve is defined by curvature, and a local search algorithm based on curvature is proposed to realize the classification of the length of the side hair. According to the curvature of curves and empirical distance of the local region search method to achieve the classification of braided hair. The feasibility of the above method is verified by experiments. The experimental results show that the improved ASM method has achieved better feature point location results, and the hairstyle classification algorithm in this paper has also obtained better hairstyle classification results. Finally, the shortcomings of face feature extraction and hairstyle classification are analyzed and further research directions are proposed.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
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