MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

ECCV, 2016.

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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org
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We provide concrete measurement set for people to evaluate the model performance and provide, to the best of our knowledge, the largest training dataset to facilitate research in the area

Abstract:

In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this indivi...More

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Introduction
  • The authors design a benchmark task as to recognize one million celebrities from their face images and identify them by linking to the unique entity keys in a knowledge base.
  • The current face identification task mainly focuses on finding similar images for the input image, rather than answering questions such as “who is in the image?” and “if it is Anne in the image, which Anne?”.
  • This lacks an important step of “recognizing”.
Highlights
  • In this paper, we design a benchmark task as to recognize one million celebrities from their face images and identify them by linking to the unique entity keys in a knowledge base
  • – We provide the following datasets,2 – One million celebrities selected from freebase with corresponding entity keys, and a snapshot for freebase data dumps; – Manually labeled measurement set with carefully designed evaluation protocol; – A large scale training dataset, with face region cropped and aligned
  • We provide concrete measurement set for people to evaluate the model performance and provide, to the best of our knowledge, the largest training dataset to facilitate research in the area
  • The images in our training dataset are associated with entity keys in knowledge base, of which the gender information could be retrieved
  • People could train a robust gender classifier for the face images in the wild based on this large scale training data
  • We look forward to exciting research inspired by our training dataset and benchmark task
Results
  • Evaluation Protocol

    The authors evaluate the performance of the proposed recognition task in terms of precision and coverage using the settings described as follows.

    Setup The authors setup the evaluation protocol as follows.
  • The chance to include the measurement images in the training set is relatively low, as long as the celebrity list in the measurement set is hidden
  • This is different from most of the existing face recognition benchmark tasks, in which the measurement set is published and targeted on a small group of people.
Conclusion
  • Discussion and Future work

    In this paper, the authors have defined a benchmark task which is to recognize one million celebrities in the world from their face images, and link the face to a corresponding entity key in a knowledge base.
  • People could adopt one of the cutting-edge unsupervised/semisupervised clustering algorithms [21] [22] [23] [24] on the training dataset, and/or develop new algorithms which can accurately locate and remove outliers in a large, real dataset
  • Another interesting topic is the to build estimators to predict a person’s properties from his/her face images.
  • The authors look forward to exciting research inspired by the training dataset and benchmark task
Summary
  • Introduction:

    The authors design a benchmark task as to recognize one million celebrities from their face images and identify them by linking to the unique entity keys in a knowledge base.
  • The current face identification task mainly focuses on finding similar images for the input image, rather than answering questions such as “who is in the image?” and “if it is Anne in the image, which Anne?”.
  • This lacks an important step of “recognizing”.
  • Results:

    Evaluation Protocol

    The authors evaluate the performance of the proposed recognition task in terms of precision and coverage using the settings described as follows.

    Setup The authors setup the evaluation protocol as follows.
  • The chance to include the measurement images in the training set is relatively low, as long as the celebrity list in the measurement set is hidden
  • This is different from most of the existing face recognition benchmark tasks, in which the measurement set is published and targeted on a small group of people.
  • Conclusion:

    Discussion and Future work

    In this paper, the authors have defined a benchmark task which is to recognize one million celebrities in the world from their face images, and link the face to a corresponding entity key in a knowledge base.
  • People could adopt one of the cutting-edge unsupervised/semisupervised clustering algorithms [21] [22] [23] [24] on the training dataset, and/or develop new algorithms which can accurately locate and remove outliers in a large, real dataset
  • Another interesting topic is the to build estimators to predict a person’s properties from his/her face images.
  • The authors look forward to exciting research inspired by the training dataset and benchmark task
Tables
  • Table1: Face recognition datasets
  • Table2: Experimental results on the 500 published celebrities
Download tables as Excel
Related work
  • Typically, there are two types of tasks for face recognition. One is very wellstudied, called face verification, which is to determine whether two given face images belong to the same person. Face verification has been heavily investigated. One of the most widely used measurement sets for verification is Labeled Faces in the Wild (LFW) in [7,8], which provides 3000 matched face image pairs and 3000 mismatched face image pairs, and allows researchers to report verification accuracy with different settings. The best performance on LFW datasets has been frequently updated in the past several years. Especially, with the “unrestricted, labeled outside data” setting, multiple research groups have claimed higher accuracy than human performance for verification task on LFW [4,9].
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