年齡估計算法實現分層方法（圖 10）。首先，將輸入片段分為三個年齡組： 18 歲以下，18—45 歲，45 歲以上。其次，將這一步的結果細分為七個較小的組，每個小組限制為一個十年。因此，多類別分類的問題被減至一組二進制「一對全部」分類器（BC）。然後，分類器會根據關聯的類別對圖像進行排名，並透過分析這些軼直方圖來取得最終決定。
這些 BC 通過使用兩級方法構建。如前所述，第一次轉移至自適應特徵空間後，使用具有 RBF 核心的支持向量機來分類圖像。
針對亮度特性輸入片段對輸入片段進行預處理，以便對齊並轉換為均勻的比例。此預處理步驟包含顔色-空間轉換和縮放，這兩項作業與性別辨識算法中使用的作業類似。計算每個顏色分量的特徵，並對它們進行組合以形成統一的特徵向量。
訓練和測試要求具備足夠大的顔色圖像數據庫。我們將最先進的 MORPH 和 FG-NET 圖像數據庫與我們自己從不同來源獲得的圖像數據庫結合在一起，包括 10,500 張人臉圖像。利用AadBoost 臉部偵測演算法自動偵測圖像中的臉孔。
共使用 7000 張圖像來訓練和測試第一階段的年齡分類演算法。利用 144 個自適應特徵建立了三個 BC。
第一階段的分類結果顯示，年輕臉孔的準確度為 82%，中年臉孔的準確度為 58%，老年臉孔的準確度為 92%。三個年齡組別的總體年齡分類準確度為 77.3%。
第二階段的 BC 的建構方式與第一階段相同 (如上所述)。圖 11 顯示建議的演算法第一階段的年齡估計視覺範例。
翻譯: 您學科領域的翻譯師翻譯您的原稿
The age estimation algorithm realises hierarchical approach (fig. 10). First of all input fragments are divided for three age groups: smaller than 18 years old, from 18 - 45 years old and bigger than 45 years old. Afterwards the results of this in the first stage are more subdivided to seven newer groups with each limiting to one single decade. Thus the problem of multiclass classification is therefore reduced to a set of binary ‘one-against-all’ classifiers (BC).These classifiers calculate: ranks of each of the analyzed class. The total decision is obtained then by the analysis the previously received histogram of ranks.
These BCs are construction is applied with the transitioning to adaptive feature space, equal to this described earlier, and support vector machines classification of images with RBF kernel.
Input fragments were preprocessed for their luminance characteristics to align and to transform them to uniformal scale. Preprocessing includes color-space transformation and scaling, both similar to that of gender recognition algorithm. Features, calculated for each colour component, are combined to form a uniform featured vector.
Training require a huge enough coloring image database: We used state-of-the-art image databases MORPH and FG-NET with our own image database, gathered from some sources, which comprised of 10500 faces. Faces on the images were detected automatically by AdaBoost face detection algorithms.
A total number of seven thousand images were used for age classification algorithm training and testing on the first stage. 3 binary classifiers were made utilizing 144 adaptive features each of.
Classification results on the first stage are: 82 % accuracy for young age, 58 % accuracy for middle age and 92 % accuracy for senior age. Age classification rate in a three age division problem – 77.3 %.
Binary classifiers of the second stage were constructed equal to the first stage described above. A visual example of age estimation by the proposed algorithm on its first stage is presented in figs. 11.
雙語核對：雙語核對師依照原文檢查譯文是否正確，並修正錯誤
The age
estimation algorithm realises hierarchical approach (fig. 10). First of all input
fragments are divided into1for three age groups: smaller than 18 years old,
from 18 - 45 years old and bigger than 45 years old. Afterwards the results of this
in the first stepage are more subdivided to seven smallernewer
groups, with
each limiteding
to one single decade. Thus the problem of multiclass classification is therefore
reduced to a set of binary ‘one-against-all’ classifiers (BC).These classifiers
calculate: ranks of each of the analyzed class. The total decision is obtained then
by the analysis the previously received rank histograms of ranks.
These BCs are
constructioned
using a two-level approach. After is appliedfirst with the 2transitioning to adaptive feature space,
asequal to this3
described earlier, and support vector machines classification of images with
RBF kernel.
Input fragments were
preprocessed for their luminance characteristics to align and to transform them
to uniformal scale. This pPreprocessing step includes
color-space transformation and scaling, both operations
similar to those used inthat of a gender
recognition algorithm. Features, calculated for each colour
component, are combined to form a uniform featured4
vector.
Training and testing5 require a sufficientlyhuge largeenough coloring
image database: We used state-of-the-art image databases MORPH and FG-NET with our
own image database, gathered from some6many sources, which comprised of 10,500 face images.
Faces on the images were detected automatically by AdaBoost face detection
algorithms.
A total number
of seven thousand images were used for age classification algorithm training
and testing on the first stage. 3 binary classifiers were made utilizing 144
adaptive features each of.
Classification
results on the first stage are: 82 % accuracy for young age, 58 % accuracy for
middle age and 92 % accuracy for senior age. Age classification accuracyrate
in a three age division problem – 77.3 %.
Binary classifiers
of the second stage were constructed equalsame to the first stage described above. A visual
example of age estimation by the proposed algorithm on its first stage is
presented in figs. 11.
編修：英文母語編修師改善文章整體的流暢度與呈現方式
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visual example of age estimation by the first stage
of the proposed algorithm on its first
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完稿：翻譯完成品準時遞交給客戶
The proposed age estimation algorithm realizes hierarchical approach (Fig. 10). First, the input fragments are divided into three age groups: less than 18 years old, 18–45 years old, and more than 45 years old. Second, the results of this first step are further subdivided into seven smaller groups, each limited to a single decade. This reduces the original multiclass classification problem to a set of binary “one-against-all” classifiers (BC). Each classifier then ranks the images based on the associated class, and the final decisions are obtained by analyzing these rank histograms.
These BCs are constructed using a two-level approach. After first transitioning to an adaptive feature space, as described earlier, the images are classified using support vector machines with radial basis function(RBF) kernels.
The input fragments are preprocessed to align and transform their luminance characteristics to a uniform scale. This preprocessing step includes color-space transformation and scaling, both operations similar to those used in the gender recognition algorithm. Features are calculated for each color component and combined to form a uniform feature vector.
Training and testing require a sufficiently large color image database. Here, we combined the state-of-the-art MORPH and FG-NET image databases with our own image database, gathered from many sources and comprising 10,500 face images. The faces in the images were detected automatically by the AdaBoost face detection algorithms.
A total of 7000 images were used to train and test the first stage of the age classification algorithm. Three BCs were created, each with 144 adaptive features.
The first-stage classification results showed 82% accuracy for young faces, 58% accuracy for middle-aged faces, and 92% accuracy for elderly faces. The overall age classification accuracy for the three age categories was 77.3%.
The second-stage BCs were constructed in the same manner as the first stage (described above). Fig. 11 shows a visual example of age estimation by the first stage of the proposed algorithm.
