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SCIENCE CHINA Information Sciences, Volume 63, Issue 2: 120102(2020) https://doi.org/10.1007/s11432-019-2733-4

Ordinal distribution regression for gait-based age estimation

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  • ReceivedJul 18, 2019
  • AcceptedNov 12, 2019
  • PublishedJan 15, 2020

Abstract

Computer vision researchers prefer to estimate age from face images because facial features provide useful information. However, estimating age from face images becomes challenging when people are distant from the camera or occluded. A person's gait is a unique biometric feature that can be perceived efficiently even at a distance. Thus, gait can be used to predict age when face images are not available. However, existing gait-based classification or regression methods ignore the ordinal relationship of different ages, which is an important clue for age estimation. This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation. Specifically, we decompose gait-based age regression into a series of binary classifications to incorporate the ordinal age information. Then, an ordinal distribution loss is proposed to consider the inner relationships among these classifications by penalizing the distribution discrepancy between the estimated value and the ground truth. In addition, our neural network comprises a global and three local sub-networks, and thus, is capable of learning the global structure and local details from the head, body, and feet. Experimental results indicate that the proposed approach outperforms state-of-the-art gait-based age estimation methods on the OULP-Age dataset.


Acknowledgment

This work was supported in part by National Natural Science Foundation of China (Grant No. 61673118), Shanghai Municipal Science and Technology Major Project (Grant No. 2018SHZDZX01), ZJLab, and Shanghai Pujiang Program (Grant No. 16PJD009). We are grateful to the reviewers and the Associate Editor for their constructive comments.


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  • Figure 1

    (Color online) GEIs of subjects of different age and gender in the OULP-Age dataset. The number above each GEI indicates the corresponding age of the subject.

  • Figure 2

    (Color online) Predictive probability of the $k$-th classifier should not be greater than that of the ($k-1$)-th classifier on an ordinal distribution. Both A and B have the same cross-entropy loss; however, B is preferable to A on an ordinal distribution.

  • Figure 3

    (Color online) Structure of the proposed ODR-GLCNN. The output layer contains $K-1$ binary classifications incorporating the ordinal information into the current end-to-end learning process.

  • Figure 4

    (Color online) Age and gender distribution for the OULP-Age dataset. There are 63846 well-labeled GEIs (31093 males and 32753 females, age 2 to 90 years).

  • Figure 5

    (Color online) Comparisons of age estimation CSs by the proposed approach and state-of-the-art methods on the OULP-Age dataset.

  • Figure 6

    (Color online) Examples of gait-based age estimation results by using the proposed approach on the OULP-Age dataset. The top three rows show nine successful age estimation examples (MAE smaller than 5 years) for young, middle-aged, and old subjects, respectively. The last row shows nine failure cases (MAE larger than 20 years). The numbers above each image show the subject's ground-truth age and estimated age, respectively.

  • Figure 7

    (Color online) Feature visualization of CNN (a) and GL-CNN (b). Network features are reduced from 1024 dimensions to 2 dimensions by a $t$-SNE technique. Ages are divided into nine groups. Different colors represent different age groups.

  • Figure 8

    (Color online) Multi-task structure for age and gender estimation tasks.

  • Table 1   Comparison of the age estimation MAEs with the proposed approach and state-of-the-art methods on the OULP-Age dataset
    Method MAE CS ($k=5$) (%)
    SVR [36] 7.66 41.40
    MLG [7] 10.98 43.40
    OPLDA [9] 8.45 36.50
    OPMFA [9] 9.08 34.70
    GPR [8] 7.30 43.60
    ASSOLPP [24] 6.78 53.00
    VGG16 + Mean-Variance [4] 5.59 60.46
    ODR-GLCNN (ours) 5.12 66.95
  • Table 2   MAE results on the two subsets of the USF dataset using different methods
    MethodGalleryProbe A
    MAE CS ($k=5$) (%) MAE CS ($k=5$) (%)
    SVR [36] 8.21 37.50 7.83 41.70
    MLG [7] 9.45 32.80 9.06 34.40
    OPLDA [9] 7.05 43.70 6.76 51.20
    OPMFA [9] 6.95 47.30 6.62 52.00
    ASSOLPP [24] 6.81 50.50 6.48 52.70
    VGG16 + Mean-Variance [4]6.10 54.20 5.93 56.30
    ODR-GLCNN (ours) 5.91 56.60 5.75 59.40
  • Table 3   Comparisons among different CNN-based methods on the OULP-Age dataset$^{\rm~a)}$
    Network MAE CS ($k=5$) (%) Time (ms)
    CNN 5.45 64.64 7.27 $\times~10^{-2}$
    VGG16 5.63 63.92 21.9 $\times~10^{-2}$
    GL-CNN (ours) 5.24 65.96 8.99 $\times~10^{-2}$
  • Table 4   Comparison of different losses with the proposed GL-CNN on the OULP-Age dataset
    Loss MAE CS ($k=5$) (%)
    Euclidean 6.73 52.95
    MAE 6.65 55.16
    Squared EMD[13] 6.39 58.34
    Cross-Entropy 5.24 65.96
    ODL (ours) 5.12 66.95
  • Table 5   Influence of human gender for gait-based age estimation in terms of age MAE and gender accuracy
    Method w/o gender w/ gender Accuracy (%)
    CNN + Euclidean 6.96 6.82 96.70
    CNN + Cross-Entropy 5.40 5.34 97.20
    VGG16 + Mean-Variance 5.59 5.52 96.70
    ODR-GLCNN (ours) 5.12 5.06 97.80
  • Table 6   Comparison between proposed approach and state-of-the-art methods on the MORPH Album II dataset in terms of MAE and CS values$^{\rm~a)}$
    Method MAE CS ($k=5$) (%) Protocol
    OR-CNN [3] 3.27 73.0* RS
    DEX [38] 3.25 RS
    Ranking-CNN [2] 2.96 85.0* RS
    VGG16 + Mean-Variance [4] 2.41 90.0* RS
    AGEn [40] 2.93 RS
    dLDLF [39] 2.24 RS
    DRFs [31] 2.17 91.3 RS
    BridgeNet [41] 2.38 RS
    VGG16 + ODL ($\lambda=0$) 2.30 91.1 RS
    VGG16 + ODL ($\lambda=0.25$) (ours)2.16 92.9 RS

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