Quantifying Facial Age by Posterior of Age Comparisons

31 Aug 2017  ·  Yunxuan Zhang, Li Liu, Cheng Li, Chen Change Loy ·

We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated by observations that it is easier to distinguish who is the older of two people than to determine the person's actual age. Given a reference database with samples of known ages and a dataset to label, we can transfer reliable annotations from the former to the latter via human-in-the-loop comparisons. We show an effective way to transform such comparisons to posterior via fully-connected and SoftMax layers, so as to permit end-to-end training in a deep network. Thanks to the efficient and effective annotation approach, we collect a new large-scale facial age dataset, dubbed `MegaAge', which consists of 41,941 images. Data can be downloaded from our project page mmlab.ie.cuhk.edu.hk/projects/MegaAge and github.com/zyx2012/Age_estimation_BMVC2017. With the dataset, we train a network that jointly performs ordinal hyperplane classification and posterior distribution learning. Our approach achieves state-of-the-art results on popular benchmarks such as MORPH2, Adience, and the newly proposed MegaAge.

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Introduced in the Paper:


Used in the Paper:

MS-Celeb-1M MegaFace MORPH Adience

Results from the Paper

Ranked #6 on Age Estimation on MORPH Album2 (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Age And Gender Classification Adience Age MegaAge Accuracy (5-fold) 56.01 # 10
Age Estimation MORPH Album2 MegaAge (w. IMDB-WIKI) MAE 2.52 # 6
Age Estimation MORPH Album2 MegaAge MAE 2.87 # 9