Gait-based age estimation has been extensively studied for various applications because of its high practicality. In this paper, we propose a gait-based age estimation method using convolutional neural networks (CNNs). Because gait features vary depending on a subject’s attributes, i.e., gender and generation, we propose the following three CNN stages: (1) a CNN for gender estimation, (2) a CNN for age-group estimation, and (3) a CNN for age regression. We conducted experiments using a large population gait database and confirm that the proposed method outperforms state-of-the-art benchmarks.
Notes
Atsuya Sakata and Noriko Takemura contributed equally to this work.
Abbreviations
CNN
Convolutional neural network
CS
Cumulative score
GEI
Gait energy image
GPR
Gaussian process regression
MAE
Mean absolute error
MLG
Multilabel-guided subspace
OPLDA
Ordinary preserving manifold
OULP-Age
The OU-ISIR gait database, Large Population Dataset with Age
1 Introduction
Age estimation methods based on image processing have been extensively studied for various applications. Most of these studies focus on the images of faces, which tend to become more wrinkled and sag with age [1‐6]. However, because high-resolution full-face images are required for these age estimation methods, they can only be used in situations where human images are captured at a short distance, e.g., age confirmation for purchasing alcohol and cigarettes or in digital signage applications.
In contrast, gait features, which represent a human’s manner of walking, can be captured at a distance from an uncooperative subject. The way a human walks differs depending on his/her attributes, such as gender, physique, muscle mass, and age. From the medical view point, there are some studies on gait analysis to measure fatigue and detect disease [7, 8]. In the field of informatics, in contrast, gait-based human identification has been intensively studied for various applications such as access control, surveillance, and forensics [9‐11]. Gait differs depending on not only attributes but also individuals. For instance, individual features greatly depend on posture, stride length, arm-swinging width, and the asymmetry of walking, which is formed from habits such as holding a shoulder bag on a fixed side. Moreover, gait identification has already been used in practical cases in criminal investigations [12‐14]. Hence, we expect that gait features will be useful for age information, and we investigated gait-based age estimation.
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Gait-based age estimation expands the scope of real-world applications such as wide-area surveillance and the detection of lost children and wandering elderly people, as well as marketing research in large-scale facilities (e.g., shopping malls, terminals, and airports). There are several studies on gait-based age estimation. Makihara et al. [15] proposed an age regression algorithm based on Gaussian process regression (GPR). Lu et al. [16] proposed a multilabel-guided subspace to better characterize and correlate age and gender information, and Lu et al. [17] proposed an ordinary preserving manifold analysis (OPLDA) for gait-based age estimation. These methods unfold an image-based gait feature into a feature vector, where each dimension corresponds to each pixel. Because spatial proximity in the image structure is never considered, these methods can easily result in overtraining. To prevent this, we propose an age estimation approach using a convolutional neural network (CNN) that considers spatial proximity using a convolution operation and has had great success in many image recognition research areas.
Ideally, it is possible to achieve end-to-end learning by CNNs, i.e., any model can be trained by feeding raw images to the CNN. However, in practice, it is not easy to train networks in such an ideal situation. For this reason, existing researchers have proposed some designs in which pre-processed images are fed into the network instead of the raw images and constraints are added to the intermediate layers. In addition, recently, multi-task learning has attracted attention [18]: this method improves the accuracy of a target task by simultaneously learning target and other recognition tasks related to the target task. However, this method can instead worsen the accuracy of target tasks if other tasks adversely affect them because the model is trained to improve all the recognition tasks simultaneously.
Thus, in this paper, we propose sequential multi-task learning instead of conventional parallel multi-task learning. Each CNN for non-target tasks is trained one by one in sequence and the CNN for the target task is trained last. In this way, we can train the network to aim for the target task while taking other tasks into consideration.
Although the network architecture of sequential multi-task learning should be a deep CNN formed by chaining each CNN, we separately train each CNN, which has the same structure as those in parallel multi-task learning, in sequence to simply compare sequential with parallel multi-task by excluding the influence of the depth of the network. In other words, we predict a subject’s gender and generation beforehand and then predict an age-by-age regression model trained on the data for each gender and generation combination separately.
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We conducted a performance evaluation using the world’s largest gait database, the OU-ISIR Gait Database, Large Population Dataset with Age (OULP-Age) [19], which includ-es ages ranging from 2 to 90 years and males and females to confirm the effectiveness of the proposed method.
2 CNN-based age estimation
In this paper, the gait energy image (GEI) [20], which is a gait feature commonly used for gait-based person identification, is used as input to our CNNs. A GEI represents both dynamic features (i.e., swinging hands and legs while walking) and static features (i.e., human shapes and postures). We explain how to extract a GEI as follows. First, human silhouette sequences are obtained by background subtraction-based graph-cut segmentation. Second, we normalize silhouettes by size. Third, the gait period is detected from the normalized silhouette sequences, and finally, we generate a mean silhouette image based on the gait period.
2.1 Single CNN-based age estimation
Figure 2a shows the network structure for the CNN-based age estimator, and Table 1 shows the layer configurations. GEIs are fed into the CNN that contains two triplets of a convolution (conv) layer, batch normalization (norm) layer, and max pooling (pool) layer. It also consists of a pairs of a fully connected (fc) layer and a norm layer, and a fc layer for recognition task. The conv layers and fc layers are followed by a ReLU activation function. We call a chain of layers from the input to norm3 in Single-CNN (a blue block shown in Fig. 2a) the Conv block.
Table 1
Layer configurations of Single-CNN
Layer
#Kernels
Size/stride
#Output nodes
conv1
81
5 × 5/1
–
pool1
–
3 × 3/2
–
conv1
45
7 × 7/1
–
pool2
–
2 × 2/2
–
fc3
–
–
1024
fc4_age
–
–
1
We initialize the weight parameters of the CNN in all layers using He’s method [21] and neuron biases with a constant of 0. We train our models using Adam with an initial learning rate of 0.001. We use dropout in the fc3 and fc4 layers with a probability of 0.8 and 0.5, respectively. The output of the final layer is considered to be the predicted age. We train the age estimator to minimize the mean absolute error (MAE) between the predicted and ground truth ages.
As mentioned in [9], in recognition tasks, variations in the input GEIs are smaller than those for a common object recognition task. Therefore, even such a shallow network can represent the feature of a subject’s age.
2.2 Multistage CNN-based age estimation
Figure 1 shows the mean GEIs in the gait database (OULP-Age) for each gender and age group. It shows that gait features, e.g., human head-to-body ratio, hairstyles, shapes, and postures, vary depending on a subject’s gender and generation.
Fig. 1
Mean GEIs for each gender and age group
Fig. 2
The architectures of (a) Single-CNN and (b) Sequential multi-CNN
×
×
Thus, age estimation accuracy should improve in an age estimator based on specific genders and generations. In this paper, we attempt to improve age estimation using a multistage CNN composed of three CNN-based estimators, i.e., a gender estimator, age-group estimator, and age estimator (see Fig. 2b). Note the order of gender discrimination.
As shown in Fig. 2b, we used Conv blocks for all three estimators. For the gender estimator, the sigmoid normalized cross-entropy is employed as the loss function. For the age-group estimator, the number of outputs of the fc4 layer is changed to five (the number of age groups) and the softmax normalized cross-entropy is employed as a loss function.
2.2.1 Learning method
The learning procedure for multi-CNN age estimation (sequential multi-task CNN) is as follows (Fig. 2):
1
Train a gender estimator on a training set that includes all genders and all age groups
2
Predict gender by feeding the same training data set of (1) into the trained gender estimator
3
Train an age-group estimator for each predicted gender using the gender-predicted data from (2)
4
Predict the age group for each predicted gender by feeding the gender-predicted data from (2) into the trained age-group estimator for that gender
5
Train an age estimator for each predicted gender and each predicted age group using the data predicted in (4)
We train age estimators for each of the predicted gender and age-group estimators. Because of the decrease in the number of training data caused by this approach, overfitting can occur easily. To prevent this, we fine-tune pre-trained models. Specifically, the age-group estimator for each gender is trained by fine-tuning the age-group estimator trained on all gender data, and the age estimator for each gender and each age group is trained by fine-tuning the age estimator trained on the all age-group data for each age.
2.2.2 Definition of age-group classes
We describe how we define age-group classes for the age-group estimator in multi-CNN age estimation. Gait data in OULP-Age are divided into several age groups based on GEI similarity.
First, we divided OULP-Age into intervals of 5 years and generated a mean GEI for each group. Note that samples over 60 years old were put into the same group because of a shortage of elderly persons’ data. Second, we calculated the L2 distance between the mean GEIs of adjacent groups (Fig. 3). The L2 distance is calculated as
L2 distance between mean GEIs of adjacent age groups (OULP-Age). Silhouette images below the age-group labels represent mean GEI corresponding to each group
×
where x and y are the mean GEIs of adjacent groups with height H and width W, respectively. Finally, we defined groups with an L2 distance that is less than a threshold as the same class and designed five classes: 0–5, 6–10, 11–15, 16–60, and over 60 years.
As we mentioned in Section 2, a GEI represents both dynamic features (i.e., swinging hands and legs while walking) and static features (i.e., human shapes and postures). Because people under 15 years old are growing swiftly, they change their static features substantially, and their GEIs have remarkable differences according to age. In contrast, as shown in Fig. 3, GEIs extracted from people who are between 15 to 60 years old almost do not appear to have changing features because they have almost stopped growing up. In other wards, differences between statistic feature of GEI are more significant than those of dynamic feature. Poor accuracy during age-group estimation affects the next age regression stage, so we decided to split the age range into five age groups so that the CNNs can estimate age from the GEIs fairly precisely.
3 Performance evaluation
3.1 Database
The OU-ISIR Gait Database, Large Population Dataset with Age (OULP-Age) [19] was used to evaluate the performance of the age estimation method. OULP-Age is the world’s largest gait database that includes age and gender information. It consists of 63,846 gait images (31,093 males and 32,753 females) with ages ranging from 2 to 90 years. Figure 4 shows examples of the data, and Fig. 5 shows the distribution of subjects’ age and gender in OULP-Age. Each subject walking from the right side to the left side along the walking course is captured by a USB camera set at a position 4 m away from the walking course. More information about the data capture is given in detail in [22]. GEIs of 88 ×128 pixels extracted for a side-view gait are provided for each subject. We split the database into testing, training, and validation set at the ratio of 5:4:1, respectively. Note that 20% of the training set is used as the validation set. Tables 2 and 3 show the number of subjects among age groups and genders in the training set and testing set, respectively.
where w denotes the weight parameter matrix of the network, In is the input image, N is the number of data, M is the number of classes, y(In;w)m is the mth element of the output vector, and tnm denotes the ground truth class. The age estimation task is optimized by minimizing the mean absolute error between the ground truth and predicted age and is calculated as
where w denotes the weight parameter matrix of the network, In is the input image, N is the number of data, y(In;w) is the predicted age, and tn is the ground truth age of the nth sample. For training each network included in the proposed method with back-propagation, we use Adam [23]. We also use a batch size of 128 samples, and the initial learning rate is 0.001, which is the default value for Adam. The maximum number of epochs is 100, although we used the weights of the network at the epoch when the validation error is the minimum. Table 4 shows the distribution among the gender, and Table 5 shows the distribution among the age groups.
Table 4
Results for gender estimation with the training set
Ground truth
Total
Male
Female
Predicted
Male
15,470
165
15,635
Female
126
16,162
16,288
Total
15,596
16,327
31,923
Table 5
Result for age-group estimator on the training set
Ground truth
Total
0–5
6–10
11–15
16–60
Over 60
(a) Predicted as male
Predicted
0–5
404
74
1
0
0
479
6–10
10
2200
161
2
0
2373
11–15
0
76
1459
77
5
1617
16–60
0
7
485
10,120
119
10,731
Over 60
0
0
3
73
359
435
Total
414
2357
2109
10,272
483
15,635
(b) Predicted as female
Predicted
0–5
405
19
0
0
0
424
6–10
11
2316
57
1
0
2385
11–15
0
35
1919
83
0
2037
16–60
0
13
288
10,776
40
11,117
Over 60
0
0
0
11
314
325
Total
416
2,383
2,264
10,871
354
16,288
3.3 Evaluation method
The MAE, standard deviation (SD), and cumulative score (CS) are used as the evaluation criteria for the performance evaluation. MAE is calculated as
where tn and yn are the ground truth and predicted age values for the nth test sample, respectively, and N is the number of test samples. SD is calculated as follows.
where Nl is the number of samples whose MAE is within l year.
3.4 Comparison with existing methods not based on CNNs
We compared the two proposed methods with four comparison methods using the protocol described in [19]. Single-CNN : Proposed method with a single CNN Sequential multi-CNN : Proposed method with multiple CNN stages GPR [15] : GPR-based method SVR [2] : Support vector regression-based method OPLDA [17] : OPLDA-based method MLG [16] : A method that learns a multilabel-guided (MLG) subspace for human age
The MAEs and SDs of both versions of the proposed method and benchmarks are shown in Table 6. According to Table 6, the results of our CNN-based methods (Single-CNN and Sequential multi-CNN) are much better than those of the benchmarks. Furthermore, comparing the proposed methods, Sequential multi-CNN, which considers gender and age groups, improves the performance more than Single-CNN. In terms of SD, while the result of the proposed method is better than that of the existing method, there is no difference between our method and Single-CNN. This is because our method does not estimate age well for elderly people.
Table 6
MAEs and SDs for comparing the proposed methods with existing methods not based on CNNs
The CSs of Single-CNN and Sequential multi-CNN for each age group are shown in Fig. 6. As shown in the graph, Sequential multi-CNN significantly outperforms Single-CNN, especially in the 6–10, 11–15, and over 60 year groups.
Fig. 6
Cumulative scores of Single-CNN and Sequential multi-CNN
×
3.5 Sequential multi-CNN vs. parallel multi-CNN
We compared the proposed method with multiple CNN stages (Sequential multi-CNN) with a conventional multitask CNN [24] (Parallel multi-CNN). In Parallel multi-CNN, multiple tasks are learned at the same time, while exploiting commonalities and differences across tasks to improve the estimation accuracy for the task-specific models. Figure 10 shows the network architecture of Parallel multi-CNN. Note that Parallel multi-CNN consists of the same Conv block with Sequential multi-CNN and each loss weight is 1.0, except that the last layer is branched for each task (gender, age group, and age), to compare only the learning strategy, namely, sequential multi-task learning vs. parallel multi-task learning.
Table 7 shows the MAEs and SDs of Sequential multi-CNN and Parallel multi-CNN estimated in the same manner as in Section 3. The result of Sequential multi-CNN is better than that of Parallel multi-CNN. The CSs of Parallel multi-CNN and Sequential multi-CNN for each age group are shown in Fig. 7. The graph demonstrates that Sequential multi-CNN outperforms Parallel multi-CNN, as is the case for the comparison with Single-CNN.
Fig. 7
Cumulative scores of Parallel multi-CNN and Sequential multi-CNN
Table 7
MAEs and SDs for comparing the proposed method with a conventional multi-task CNN
Method
MAE [years old]
SD [years old]
Sequential multi-CNN (ours)
5.84
6.50
Parallel multi-CNN
6.23
6.61
The best result is indicated by the italic font
×
In the training phase, Sequential multi-CNN is trained to minimize a loss for each task in the order of gender, age group, and age, i.e., the target task is the last one, whereas Parallel multi-CNN is trained so as to minimize multi-task losses simultaneously. Thus, Sequential multi-CNN can be trained more intensively and efficiently for the target task. This seems to be why the result of Sequential multi-CNN is better.
4 Discussion
4.1 Distribution of the estimated ages corresponding to the actual age
Figure 8 presents a scatter plot of the estimated ages of Sequential multi-CNN with respect to the ground truth age. Each point is colored according to the estimated age groups. According to Fig. 8, when age-group estimation fails, age estimation also fails, i.e., the MAE is larger, especially when the estimated age groups are 11–15 and over 60 years.
Fig. 8
Scatter plot of the estimated ages of Sequential multi-CNN with respect to the ground truth age. The dotted line indicates where the estimated age equals the ground truth age
×
4.2 Order of learning tasks in Sequential multi-CNN
In Sequential multi-CNN, CNNs are trained in the order of gender, age group, and age. The reasons why learning is performed in this order are as follows:
Age is trained last because age estimation is the target task.
Age group is trained second to the last because age group has a stronger relationship with age.
Gender is trained first because gender is easier to recognize than age group.
Tables 8 and 9 show the confusion matrices of the results of gender and age-group estimation using the test set, respectively. These matrices show that the recognition rate of gender is higher than that of age group. More specifically, there are more than a few cases of incorrect recognition, especially for age-group estimation for pedestrians over 60 years. The proposed method has the problem that the failure of each estimation task causes successive failures in the next tasks. To avoid this, we need further studies to determine how to combine the CNNs at each stage into a single network so that it can effectively minimize the error of all the stages.
Table 8
Results for gender estimation with the testing set
Ground truth
Total
Male
Female
Predicted
Male
15,581
413
15,594
Female
316
16,013
16,329
Total
15,497
16,426
31,923
Table 9
Results for age-group estimation with the testing set
Ground truth
Total
0–5
6–10
11–15
16–60
Over 60
(a) Male
Predicted
0–5
266
212
1
1
0
480
6–10
36
1912
350
4
9
2311
11–15
0
213
1209
120
15
1557
16–60
0
17
604
9969
244
10,834
Over 60
0
0
9
137
266
412
Total
302
2354
2173
10,231
534
15,594
(b) Female
Predicted
0–5
334
132
1
0
0
467
6–10
108
2178
317
17
0
2620
11–15
0
182
1099
271
3
1555
16–60
0
66
756
10,367
257
11,446
Over 60
0
2
5
81
153
241
Total
442
2560
2178
10,736
413
16,329
4.3 Difference of accuracy between male and female
Table 10 shows the gender-specific MAEs and SDs of Sequential multi-CNN, and Fig. 9 shows the graph of gender-specific CSs. As shown in Table 10 and Fig. 9, both MAE and SD of female subjects are worse than those of male subjects overall, especially over 60. Moreover, the CSs of Sequential multi-CNN is worse than that of Single-CNN in the case of 11–15 age group.
Fig. 9
Gender-specific cumulative scores of Sequential multi-CNN. a Male. b Female
Fig. 10
Network architecture of the conventional multi-task CNN (Parallel multi-CNN)
Table 10
Gender-specific MAEs and SDs of Sequential multi-CNN
Gender
MAE [years old]
SD [years old]
Male
5.60
6.19
Female
6.07
6.77
The best result is indicated by the italic font
×
×
This is because the female-specific personal features such as hairstyle and clothes (e.g., skirt and one-piece) affect the accuracy of age estimation. It is easy to estimate age of both male and female children due to distinctive features such as height. Adult female, in contrast, have more variations in hairstyle and clothes than adult male. Therefore, it is more difficult to estimate the age of female than that of male in adult generation.
4.4 Applicability of sequential multi-task learning to other tasks
In this paper, it was confirmed that sequential multi-task learning is more effective for age estimation than CNN-based single task learning and parallel multi-task learning (Fig. 10). The framework of sequential multi-task learning can be applied not only to age estimation but also to other recognition tasks, e.g., person identification and health estimation. Therefore, various applications of the sequential multi-task learning can be expected in both the medical and information-science fields.
5 Conclusion
In this paper, we proposed a gait-based age estimation method using CNNs. To estimate ages based on differences in gait features depending on gender and generation, we proposed a method composed of three stages of CNNs: a gender estimator, an age-group estimator, and an age estimator. The results of the experiments using a large-scale gait database (OULP-Age) yielded an MAE of 5.84 years, which outperforms the benchmarks.
In the future, we plan to perform two studies to enhance age estimation. First, as mentioned in Section 4.2, we will train a deeper network formed by chaining CNNs for several tasks instead of a combination of sequential CNNs. In this way, we can avoid degrading the accuracy of the proposed method due to the incorrect recognition of each task. Second, we need to collect more gait data because the database we used lacks data on elderly subjects. By doing this, we will be able to improve our method for all generations.
Acknowledgements
Not applicable.
Funding
This work was supported by JST-Mirai Program JPMJMI17DH.
The authors declare that they have no competing interests.
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