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A new method to estimate ages of facial image for large database

机译：大型数据库中估计人脸图像年龄的新方法

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As a common consensus that the appearances of different persons with the same age diverge widely, we have an opinion that the estimated result of a facial image should be a dynamic range or discrete candidate ages, not a specific one or classified into predefined age groups. Therefore, this paper presents a new method to estimate a set of possible ages of a facial image for large image database with a novel measurement. Firstly by transferring the shape and appearance features of a face into a set of Landmark-Terms, then the famous technology TFIDF in information retrieval and text mining fields is introduced to build a weight matrix of these Landmark-Terms for all age groups, and then some possible ages of a facial image are estimated by this matrix. Secondly, a new clustering method is also used to find the density peaks for each age group by processing the LBP features, then according to the distances of the facial image to the peaks, we obtain another possible estimated ages. Thirdly, we find the first density peak among the two sets of possible ages mentioned above, then choose those ages whose distances to the peak age are short enough in the two set as final estimated ages. Finally, a novel measurement is proposed to evaluate the performance for methods that provide more than one possible estimated ages. The experiments show that our method is promising, the best MAE and CS are close to the best performance of state-of-the-art, and the best BPMAE and NBPMAE also indicate the top possible ages could cover the neighborhood of the the ground-truth age with small errors, in other words, it narrows the age scope effectively.
机译：作为一个普遍共识，即同一年龄段的不同人的外貌差异很大，我们认为面部图像的估计结果应该是动态范围或离散的候选年龄，而不是特定的年龄或分类为预定的年龄组。因此，本文提出了一种新方法来估计具有新颖度量的大图像数据库的面部图像的可能年龄集。首先通过将人脸的形状和外观特征转换为一组地标术语，然后引入信息检索和文本挖掘领域的著名技术TFIDF，为所有年龄段的人构建这些地标术语的权重矩阵，然后通过该矩阵估计面部图像的一些可能年龄。其次，还使用一种新的聚类方法通过处理LBP特征来找到每个年龄组的密度峰值，然后根据面部图像到峰值的距离，获得另一个可能的估计年龄。第三，我们在上述两个可能的年龄组中找到第一个密度峰值，然后选择在这两个年龄组中与峰值年龄的距离足够短的那些年龄作为最终估计年龄。最后，提出了一种新颖的评估方法，以评估提供多个可能估计年龄的方法的性能。实验表明，我们的方法很有希望，最好的MAE和CS接近最先进的性能，最好的BPMAE和NBPMAE也表明最高年龄可能覆盖了地面附近地区-误差很小的真实年龄，换句话说，它有效地缩小了年龄范围。

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