對LFW數據庫的翻譯

原文：
Training, Validation, and Testing:
View 1: For development purposes, we recommend using the below training/testing split, which was generated randomly and independently of the splits for 10-fold cross validation, to avoid unfairly overfitting to the sets above during development. For instance, these sets may be viewed as a model selection set and a validation set. See the tech report below for more details.
Explore the sets: [training][test]
Download the sets: pairsDevTrain.txt, pairsDevTest.txt, peopleDevTrain.txt, peopleDevTest.txt

譯文：
訓練， 驗證和測試
視角1： 處于發展的目的，我們推薦使用如下的訓練/測試 分割， 隨機生成且獨立的10組交叉驗證的分法，可以防止在優化過程中，出現過度擬合的現象。舉例，這些集合能被當做模型選擇集和驗證集。 看如下技術報告的具體說明。
看集合： [訓練集] [測試集]
下載集合： 訓練集合對.txt, 測試集合對.txt, 訓練集合人名.txt, 測試集合人名.txt

View 2: As a benchmark for comparison, we suggest reporting performance as 10-fold cross validation using splits we have randomly generated.
Explore the sets: 1 [2] [3] [4] [5] [6] [7] [8][9][10]
Download the sets: pairs.txt, people.txt
For information on the file formats, please refer to the README above.
For details on how the sets were created, please refer to the tech report below.

視角2： 作為比對基準，我們建議用隨機生成的10組交叉驗證集合，做性能報告輸出。
看集合：1 [2] [3] [4] [5] [6] [7] [8][9][10]
下載集合： 集合對.txt , 人名.txt
對于文件格式的信息，請見上面的 README文件；
對于這些集合是如何產生的，請參見下面的技術報告；

Results:
Accuracy and ROC curves for various methods available on results page.
Information:
13233 images
5749 people
1680 people with two or more images

對于幾種不同方法的精度和ROC曲線見results頁面。
信息：
13233 圖片
5749 人
1680 人，擁2張以上的圖

Errata:
The following is a list of known errors in LFW. Due to the small number of such errors, the database will be left as is (without corrections) to avoid confusion.
It is important that users of the database provide their algorithms with the database as is, i.e. without correcting the errors below, since previous results published for the database did not have the advantage of correcting for these errors.
Currently, there are five incorrectly labeled matched pairs in View 2. While we do not believe this should have a significant effect on accuracy, we do encourage researchers to be aware of these errors when producing any visualizations (e.g. matched pairs most confidently predicted as mismatched, as the matched pair may actually be mismatched).

勘誤表：
如下是LFW庫中已知的錯誤。因為只有少量的幾處錯誤，數據庫將保持原樣，以防止沖突。這對于使用這個數據庫的人來說，非常重要。例如沒有修改以下錯誤，因為先前公布的結果對于修正之處毫無優勢。
現在，在視角2里面有5處不正確標注匹配的集合，當我們不相信這將對精度有正向的影響，我們鼓勵研發者知曉這些錯誤，當要產出某些可視化的東西時。 如匹配對很大程度被判斷為不匹配，因為匹配對實際上是不匹配的。

The current known errors in View 2 are:
Fold 1: Janica_Kostelic_0001, Janica_Kostelic_0002
Fold 1: Nora_Bendijo_0001, Nora_Bendijo_0002
Fold 5: Jim_OBrien_0001, Jim_OBrien_0002
Fold 5: Jim_OBrien_0001, Jim_OBrien_0003
Fold 5: Elisabeth_Schumacher_0001, Elisabeth_Schumacher_0002
More detail about all the errors is given below.
Note: unless stated otherwise below, any error in a matched pair will mean that the label (“matched”) is wrong. Any error in a mismatched pair, even with the person having the wrong identity, will generally be correct (the label of “mismatched” will still be correct).

在視角2已知錯誤如下：
組合1： Janica_Kostelic_0001, Janica_Kostelic_0002
組合 1: Nora_Bendijo_0001, Nora_Bendijo_0002
組合 5: Jim_OBrien_0001, Jim_OBrien_0002
組合 5: Jim_OBrien_0001, Jim_OBrien_0003
組合 5: Elisabeth_Schumacher_0001, Elisabeth_Schumacher_0002
更多錯誤的具體細節如下。
備注： 除非下面另有規定， 任何在匹配對立面的錯誤，意味標簽“匹配”是錯誤的；任何在不匹配對里的錯誤，即使是對象的身份弄錯，該標簽也被認為是正確的。

…..具體的例子

Resources:
Collected resources related to LFW:
Note: We have not verified the accuracy or reliability of the code and data at the following links; we merely provide them as a convenience. Please use your own judgment about the accuracy of the resources below.

資源:
收集了LFW相關的資源：
備注： 我們沒有驗證以下這些鏈接的代碼數據的真實性，我們僅僅只是便利的提供這些數據。 請用您自己對于以下資源提供的精度，進行判斷。

LFWgender
“Getting the known gender based on name of each image in the Labeled Faces in the Wild dataset. This is a python script that calls the genderize.io API with the first name of the person in the image.”

LFWgender
知道LFW庫里的每個人的名字，這是調用了一個叫genderize.io API的python腳本，通過名字索引得到用戶的性別。

CASIA WebFace Database
“While there are many open source implementations of CNN, none of large scale face dataset is publicly available. The current situation in the field of face recognition is that data is more important than algorithm. To solve this problem, we propose a semi-automatical way to collect face images from Internet and build a large scale dataset containing 10,575 subjects and 494,414 images, called CASIA-WebFace. To the best of our knowledge, the size of this dataset rank second in the literature, only smaller than the private dataset of Facebook (SCF). We encourage those data-consuming methods training on this dataset and reporting performance on LFW. “

CASIA WebFace Database
盡管有許多開源代碼實現了CNN，但還沒有大規模數據庫可以公開獲得。 在人臉識別領域是數據比算法更重要。 為了解決這個問題，我們提供半自動的方式，去從互聯網上收集人臉圖片，建造了一個10575類，494414張圖片，即CASIA-Webface. 據我們所知，這個數據庫的容量排名第二，只小于Facebook的私有數據庫 SCF. 我們鼓勵哪些耗費數據的算法，在此數據庫上訓練，在LFW上報告性能。

LFW3D - collection of frontalized LFW images and Matlab code for frontalization

“Frontalization is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face recognition systems… we explore the simpler approach of using a single, unmodified, 3D surface as an approximation to the shape of all input faces. We show that this leads to a straightforward, efficient and easy to implement method for frontalization. More importantly, it produces aesthetic new frontal views and is surprisingly effective when used for face recognition and gender estimation.”

LFW3D –收集LFW正面的照片，并用Matlab 代碼正面化
正面化是對于單張不受限制的照中的人臉，將多張正向人臉合成。 近期的報告指出這個操作能增加人臉識別系統的性能。 我們探索了更簡單的方式，用單張，未修改的，3D表面 作為所有輸入人臉的最大近似。 我們發現這是簡單高效的正面化實現方式。 更重要的是， 它可以產生更多的正向視角，并且用于人臉識別中，異常的高效。

Contact:
Questions and comments can be sent to:
Gary Huang - gbhuang@cs.umass.edu

聯系：
有問題和建議，請發送至：
Gary Huang - gbhuang@cs.umass.edu 麻省大學計算機學院

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-原文參考 LFW官網,

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