face template 中文意思是什麼
face template
解釋
劃線樣板-
It can save the information near lips, which may be deleted by the method based on two points. ( 2 ) based on the analysis of current methods, a new multi - pose facial feature location algorithm is developed, which is based on the analysis of multi - feature and integral projection, the combination of an iterative search with a confidence function and template matching. the algorithm not only improves the location accuracy, but also speeds up a great deal. ( 3 ) based on the analysis of the advantages and disadvantages of current feature extraction methods, an adaptive facial feature selection criterion is developed, which is based on facial local feature protrusion consisting of several aspects, such as face image resolution and image quality
其後研究了人臉特徵提取，一、討論了適合於多姿態人臉識別的基於三點仿射變換的人臉圖像歸一化方法，以克服基於兩點仿射變換會引起較大圖像信息損失的缺陷；二、在分析現有器官定位演算法的基礎上，提出了新多姿態人臉器官特徵定位技術，將多特徵和直方圖分析、基於置信度函數的迭代搜索和模板匹配相結合，既提高了器官定位精度，又提高了定位速度。 -
Its basic thought is that using amass of simple classifier which has common classified ability and through thecertain method ， at last ， constitutes a very strong classifier which has strongclassified ability carries on many times with this strong classifier to the goalpictures ， finally confirmed the pictures whether includes the human face andits the general position this algorithm uses a characteristic which called haar characteristic thischaracteristic is one kind of simple rectangular characteristic ， because it issimilar with the haar wavelet ， so called haar characteristic this kind ofcharacteristic is composed of two or many rectangles that are congruent andneighboring there are white and black kinds of rectangles in the characteristictemplate, and defines this characteristic template characteristic value as thewhite rectangle this characteristic value is that the difference between white
本文主要研究基於haar特徵的adaboost演算法。由於以前提出的特徵中包含的人臉基本特徵比較少，導致檢測時間過長。本文根據人臉基本特徵的分佈提出一種新的特徵，新特徵覆蓋了人的眼睛，鼻子和嘴，它由haar特徵中的一些簡單特徵組合而成，形狀類似卷積中用到的3階矩陣，這種新特徵檢測的結果是可以檢測到人臉，時間上得到優化。 -
To retrieval directly information with images, by using the face detection technique based on the gravity - center template matching method and the face recognition technique based on methods of the principal component analysis and the linear discriminative analysis
摘要為了能直接通過圖像檢索自己所需要的信息，提出了一種直接根據人臉圖像來檢索信息的技術，採用基於重心模板匹配的人臉檢測技術與主成分分析方法、線性判別分析方法相結合的人臉識別技術設計並實現了一個人臉圖像檢索系統。 -
In training process, we use kernel - based fisher discrimination analysis ( kfda ) method to train the input sample vectors. the method has been used in face recognition and has been demonstrated better recognition capability than other methods ( pca, kpca, svm ). we calculate the optimal subspace wopt and project the sample gait sequences to wopt, then get the tracks of the sequences, calculate the track centroid and calculate the exemplar projection centroid of the sequences in the same class, and the exemplar projection centroid represents the class template. to test the class of a gait sequence, we also project the test sequence to the eigenspace, and calculate the track centroid, then calculate the euclidean distance of the test sequence tracking centroid with the sample sequences ’ exemplar projection centroids. and the class which the test sequence belongs to is the one that the sample sequence which the euclidean distance is shortest belongs to
該方法在人臉識別的研究中已有採用並在同樣測試條件下取得比其他識別方法更好的識別性能。採用kfda方法取得最優特徵空間wopt ，把步態樣本序列映射到wopt中，取得樣本序列在特徵空間中的軌跡，計算軌跡質心，把同類樣本序列的軌跡質心進行平均求得該類的標本投影軌跡質心，作為該類的模板。在識別時，將測試序列也投影到特徵空間中，取得序列軌跡質心，對測試序列軌跡質心與樣本的標本投影軌跡質心計算它們的歐氏距離。 -
In this paper, we present a multi - feature optimal fusion algorithm, inclusive of skin color, to detect one or multiple faces in color image with complex background. it is a hierarchical approach and integrates the skin color segmentation, face template matching and a neural network frontal face detector. with the elimination of false areas, the search area will become smaller and smaller, and the detection will be accomplished eventually
該演算法是一種層次式、由粗到精的檢測方法，按照「分割-搜索」的檢測模式，將膚色分割、平均臉模板匹配與神經網路驗證結合起來，採取逐步排除的方法，一步一步縮小搜索區域，實現彩色圖像中單個或多個正面端正人臉的檢測與定位。 -
Among the face tracking algorithm, we first use difference image to gain the raw - location, then in the limited position, we use two template matching to tracking face
在人臉跟蹤的演算法中，採用了在差分圖像粗定位的基礎上，在一個確定的小范圍內利用雙模板匹配進行跟蹤。 -
We bring forward locate candidate face area by mass center ; get more exactly cross correlation between candidate and template
提出利用候選人臉圖像區域和模板質心作為配準的原點，抑制人臉圖像噪聲的干擾。 -
Finally, the face template matching and bp neural network are used to verify whether the probable face area is a face indeed, and the final detection result will be obtained
最後，對得到的候選人臉區域，使用一種平均臉模板匹配與bp神經網路驗證相結合的方法進行檢測判斷，得到人臉檢測的最終結果。 -
The system combines image processing and pattern recognition techniques. we present a fast and reliable face location algorithm based on template matching
系統採用了圖像處理和模式識別等技術，提出了一種基於模板匹配的快速人臉定位演算法。 -
5. a study on face recognition for varied poses and large face database is made on the basis of automatic multi - view modeling and multi - template matching
5 、提出基於特徵的多姿態自動建模方法，結合多模板匹配策略，有效解決了大容量可變姿態的人臉識別問題。 -
Finally, this thesis build a research system based on skin color segmentation and average face template confirm, and do some experiment on it. the experimental results show that the system is feasible and effective
最後構建了基於膚色分割和模板驗證的人臉檢測試驗系統，並對該系統採用自製人臉圖像數據庫進行測試。 -
This thesis is focus on arbitrary background image which maybe contain human face, and build a research system combines the skin color segmentation, face template matching and filter candidate region by facial characteristic, some experiment have done based on the system and get a serials statistic data
本文針對復雜背景下的彩色正面人臉圖像，將膚色分割、模板匹配與候選人臉圖像塊篩選結合起來，構建了人臉檢測實驗系統，並用自製的人臉圖像數據庫在該系統下進行了一系列的實驗統計。 -
Aims at application, we mainly discuss face detection in two circumstance. ( 1 ) in the gray image, we use difference image in multi - frames, then get the edge of the face, we make the raw - location to ensure the approximate position of the face, next, we use the template of the face to occlude the pseudo face region and get the region which near the exact face region. ( 2 ) use method of two template matching, first, we get the face template from many face average, second, we copy the eye section of the face template then get the eye template
文中首先介紹了圖像預處理的各種方法，重點介紹了圖像分割和人臉檢測定位，針對實際應用的要求，著重處理了兩種情況的人像檢測定位： （ 1 ）在灰度圖像下對連續輸入的多幀進行差分，對于得到的邊緣進行處理初步確定人像的大概位置，然後採用模板匹配進行精確定位。 （ 2 ）應用雙模板匹配的方法，由多人臉平均得到人臉模板和眼睛模板，在檢測中採取了首先使用尺度相對較小的雙眼模板搜索候選人臉，再用人臉模板匹配進一步篩選候選人臉的方法。 -
In this paper, we provide a new human face detection algorithm based on template - matching, mosaic image and support vector machines
本文提出了一種基於模板匹配、馬賽克圖和支持向量機的人臉檢測演算法。 -
In this phase to solve the problem that the really face have some inclination and size is not always same, this thesis adjust the stand template thought calculate candidate face areas angle and area size. it can promote the veracity of template matching. at same time it avoid use different size template to try multi - times, the algorithm have higher efficiency
本文使用平均模板匹配方法對候選人臉進行確認，並針對圖像中的人臉通常有一定角度旋轉和尺寸大小不確定的問題，通過計算候選人臉圖像塊的偏轉角度和面積，並以此調整模板，優化模板配準，提高模板匹配的準確性，同時避免使用多尺度模板進行多次匹配運算，提高演算法效率。
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