Papers › C3AE: Exploring the Limits of Compact Model for Age Estimation

C3AE: Exploring the Limits of Compact Model for Age Estimation

10 Apr 2019CVPR 2019 6arXiv:1904.05059archive 2025-07-28

Chao Zhang, Shuaicheng Liu, Xun Xu, Ce Zhu

Age estimation is a classic learning problem in computer vision. Many larger and deeper CNNs have been proposed with promising performance, such as AlexNet, VggNet, GoogLeNet and ResNet. However, these models are not practical for the embedded/mobile devices. Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models. However, their representation has been weakened because of the adoption of depth-wise separable convolution. In this work, we investigate the limits of compact model for small-scale image and propose an extremely Compact yet efficient Cascade Context-based Age Estimation model(C3AE). This model possesses only 1/9 and 1/2000 parameters compared with MobileNets/ShuffleNets and VggNet, while achieves competitive performance. In particular, we re-define age estimation problem by two-points representation, which is implemented by a cascade model. Moreover, to fully utilize the facial context information, multi-branch CNN network is proposed to aggregate multi-scale context. Experiments are carried out on three age estimation datasets. The state-of-the-art performance on compact model has been achieved with a relatively large margin.

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Code

StevenBanama/C3AE mentioned on GitHubtf report

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Tasks

Age Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Age Estimation FGNET C3AE (WIKI-IMDB) MAE 2.95 #3 of 8 Archive leaderboard report
Age Estimation FGNET AEBFI MAE 52 #8 of 8 Archive leaderboard report

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Methods

1x1 ConvolutionAuxiliary ClassifierAverage PoolingBatch NormalizationBottleneck Residual BlockConvolutionDense ConnectionsDropoutGlobal Average PoolingGoogLeNetGrouped ConvolutionInception ModuleKaiming InitializationLocal Response NormalizationMax PoolingReLUResidual BlockResidual ConnectionSoftmax

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