Multiple Facial Attributes Estimation based on
Weighted Heterogeneous Learning
H.Fukui* T.Yamashita* Y.Kato* R.Matsui*T. Ogata** Y.Yamauchi* H.Fujiyoshi**Chubu University
1200, Matuoto-cho, Kasugai,
Aichi, Japan**Abeja Inc.
4-1-20, Toranomon, Minato-ku,
Tokyo, Japan

Abstract. To estimate multiple face attributes, independent classifier
for each attribute are trained such as facial point detection, gender recognition, and age estimation in the conventional approach. It is ineﬃcientbecause the computational cost of training and testing increases with
the number of tasks. To address this problem, heterogeneous learning isable to train a single classifier to perform multiple tasks. Heterogeneous
learning is simultaneously train regression and recognition tasks, thereby
reducing both training and testing time. However, it is diﬃcult to obtainequivalent performance for set of single task classifiers due to variance of
training error of each task. In this paper, we propose weighted heterogeneous learning of a convolutional neural network with a weighted error
function. Our method outperformed the conventional method in terms of
facial attribute recognition, especially for regression tasks such as facial
point detection, age estimation, and smile ratio estimation.

1

Introduction

Facial attributes estimation such as facial point, gender, and age has been used inmarketing strategies and social networking services. Marketing strategies recommend specific items that are matched to the client requirement. Various social
networking services based on facial recognition techniques have recently beendeveloped that can estimate age from a facial image with a high accuracy.
To estimate multiple face attributes, independent classifier for each attributes
are trained such as facial point detection, gender recognition, and age estimation.
Active appearance model (AAM)[1] and conditional regression forest (CRF)[2]are common approaches for facial point detection. Age estimation and genderrecognition are classified by a support vector machine (SVM) or a decision treeusing facial points or local binary pattern (LBP) features [3][4][5]. With the increase of deep learning, the deep convolutional neural network (CNN) [6] has
become a common classifier for facial point detection [7][8][9][10][11][12], age estimation [13][14][15][16], and gender recognition [17][18][19]. The conventional
approach must prepare multiple classifiers for each task. This approach is ineﬃcient because the computational cost of training and testing increases with
the number of tasks. To address this problem, heterogeneous learning [20] trains

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Authors Suppressed Due to Excessive Length

Fig. 1. Training error and example results by heterogeneous learning for a CNN

a single classifier to perform multiple recognition tasks. A CNN trained using
heterogeneous learning has output units that correspond to each task. Thus,a single network classifies multiple tasks simultaneously and the computational
cost does not vary with the number of tasks. In this paper, we use a heterogeneous
learning CNN for facial point detection, gender recognition, age estimation, race
recognition, and smile rate estimation.
Conventional heterogeneous learning has used the mean squared error function for regression tasks and the cross entropy error function for recognition tasks
during the training process. The error ranges of mean squared error function andcross entropy error function are noticeably diﬀerent. Therefore, we integrate theerror range from 0 to 1 by exchanging cross entropy error function for meansquared error function for recognition tasks. However, if we integrate the training error functions, diﬀerence of training error is occured, as shown in Fig. 1(a).
This diﬀerence of training error is occured by diﬀerence between label value ofregression task and recognition task. The label value of regression tasks is acontinuous value from 0 to 1, whereas the label value for classification tasks is adiscrete value of 0 or 1. Consequently, facial point detection performance suﬀers,
as shown in Fig. 1(b). Therefore, diﬀerences between training errors negatively
aﬀect the training process for heterogeneous learning for a CNN.
In this paper, we propose weighted heterogeneous learning for a CNN. First,
we select a basis task from all tasks. Additionally, we define subtasks, not includ-Title Suppressed Due to Excessive Length3

ing the basis task. We weight the error function for the subtasks. Our method
suppresses the training error and dispersion training errors by weighting the costfunction for the subtasks. Weighted heterogeneous learning for a CNN improves
the recognition performance by stable training when introducing the proposed
method.

2

Facial image analysis using heterogeneous learning
CNN

We categorize related work into facial image analysis and heterogeneous learning.
First, we describe the related publications in these categories and then further
discuss problems with existing heterogeneous learning for a CNN method asapplied to facial image analysis.
2.1

Related work

Marketing strategies and social networking services have used facial attributeinformation, such as facial point, gender, and age. In particular, facial point
have been used as features for estimating age, gender, and facial expressions.
AAM [1] is a common approach for facial point detection. AAM detects optimal facial point by changing face model parameters iteratively. AAM can detectfacial point to a high accuracy for use in training facial images. However, it isdiﬃcult for an unknown testing sample to detect facial point. The CRF proposed by Dantone et al. detects facial point using regression forests for each facepose [2]. CRF consists of two stages: the first estimates the facial pose, and thesecond regresses the facial point using regression forests. Age estimation andgender recognition are classified by a SVM or decision tree using facial point
or LBP features [3][4][5]. CNN has also become a common classifier for facial
point detection [7][8][9][10][11][12], age estimation [13][14][15][16], and genderrecognition [17][18][19].
Performing recognition or estimation for multiple tasks requires the construction of classifiers corresponding to each task. However, this is time-consuming
during training and testing, and the computation time increases with the numberof tasks. One of the methods developed to address this problem is heterogeneous
learning, which performs multiple tasks in a single network. A CNN trainedfor heterogeneous learning has units that output the recognition results corresponding to each task. The computational cost does not directly depend on thenumber of tasks. Heterogeneous learning can estimate and recognize multiple facial attribute with high accuracy by combining CNN [21][22][23][24][25]. Zhang
et al. proposed a method to perform multiple tasks such as facial point detection, gender classification, face orientation estimation, and glasses detection [21].
While the method estimated multiple tasks, its main purpose was to improve theperformance of the primary task, such as facial point detection. It thus assigned
weighted error functions to each task. When the error decreased suﬃciently, thetraining of the task was terminated earlier to avoid over-fitting to a specific task.

4Authors Suppressed Due to Excessive Length

Fig. 2. Heterogeneous learning for a CNN

2.2

Heterogeneous learning

Figure 2 shows the structure of a heterogeneous learning for a CNN. First,
M training samples are chosen randomly to form a mini-batch. We used minibatch training when updating CNN parameters. During mini-batch training,
the error E is calculated and backpropagated to update the parameters θ ofthe network. For each backpropagation[26] iteration, the samples in the minibatch are selected randomly from the dataset. When the CNN is trained using
heterogeneous learning, the recognition and regression tasks are combined ina single network and each task has an independent error function. The meansquared error in Eq.(1) and the cross entropy in Eq.(2) are employed as theerror functions of the recognition and regression tasks, respectively.
1 ∑∥ y − o ∥22
M m=1

(1)

1 ∑
−y log o
M m=1

(2)

M

EtRegression =M

EtRecognition =The errors Et of the sample m for all tasks {t|1, . . . , 1} are accumulated andpropagated once per iteration.
θ ← θ + ∆θ∑T
∂ t=1 Et
= θ−η
∂θ

(3)

The parameters θ of the CNN are updated using the diﬀerential of the accumulated error with the training coeﬃcient η.
2.3

CNN based on heterogeneous learning

Figure 1(a) shows the training errors of five tasks using heterogeneous learning
for a CNN. There are diﬀerences between the training errors for all tasks.
The diﬀerences between training errors occur because of the error function for
regression tasks and recognition tasks. There are noticeable diﬀerences between
the error ranges of the mean squared error function and the cross entropy error

Title Suppressed Due to Excessive Length

5

Fig. 3. Selection basis task

function. The mean squared error ranges from 0 to 1, and the cross entropy
error ranges from 0 to infinity. Thus, we integrate the error range from 0 to 1 byexchanging the cross entropy error function for the mean squared error function
for recognition tasks.
However, the diﬀerences between training error occur if integration errors
range from 0 to 1 by exchanging the cross entropy error function for the meansquared error function for recognition tasks. The label value of regression tasks
is a continuous value from 0 to 1, whereas the label value of recognition tasks
is a discrete value of 0 or 1. Thus, recognition tasks develop more diﬀerences
between the training errors than regression tasks. These causes negatively aﬀect
heterogeneous learning during the training process. Thus, facial point detection
performance suﬀers due to the lowest training error, as shown in Fig. 1(b).

3Proposed method

Conventional heterogeneous learning calculates the training error under Eq. (1)evenly. Hence, diﬀerences between training errors occur because of diﬀerences
between label values for regression tasks and recognition tasks. The proposed
method weights the error function of the training error Et for subtasks. The
proposed method stabilizes the training error by weighting each task and improves the heterogeneous learning performance.

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3.1

Authors Suppressed Due to Excessive Length

Training of Single task CNN

First, we obtain training error by training CNN of a single task for each task.
Unlike in training error of heterogeneous learning, the training error of a single
task CNN is not interference training error between other tasks. Therefore, wewill be able to obtain a stable basis value using training error of single task CNN,
when computing basis values for each task. In this paper, this CNN training isrepeated until the training criterion condition is satisfied.
3.2

Computing the weights of error functions

We compute basis value Nt for each task that gave weight to training error
functions using training error of single task CNN, as shown in Fig. 3. However,these training errors are varied by each iteration. Therefore, we calculate thebasis value Nt using the normal distribution of the training error for each task.
If it reflects training error for normal distribution, the normal distribution of the
training error for task t connotes 99.7% in the interval that sums the averageµ and 3-fold vertical 3σ. The other interval is the dispersion of training error,
and we can calculate the basis value Nt that is negatively aﬀected by ignoring
the interval. Thus, we use the basis value Nt that sums the average µ and 3-foldvertical 3σ.
Nt = µ + 3σ

(4)

After calculating the basis value, we select a basis task. In this paper, weselect the basis task for the lowest basis value Nt . Thus, the facial point detection
task is the basis task and the other tasks are subtasks. After selecting the basis
task and subtasks, we calculate the weight wt for each subtask. The basis value
Nf of the facial point detection task and basis value Nt of the other tasks areused in Eq. (4).
wt =

3.3

Nf
Nt

(5)

Training of weighted heterogeneous learning

We give weight to error function for each subtask, as shown in Eq. (6). The firstterm in Eq. (6) is an error function of the main task. The second term in Eq. (6)is an error function of subtasks.
(
)
M
T
−1
∑
1 ∑
2
2
E=
||yf,m − of,m ||2 +wt ||yt,m − ot,m ||2
M m=1

(6)

t=1,t̸=f

Note that yf,m and of,m are the label value and output of the facial point
detection task, respectively. Additionally, the weight wt is constant for each
iteration. We update the CNN parameters θ, such as weight filter and connection
weight, using backpropagation in Eq. (3).

Title Suppressed Due to Excessive Length

7

Table 1. Parameters of heterogeneous learning CNN structure
Image size
100 × 100
Filter size
9 × 9 × 16
2
Layer 1 Maxout
Max pooling
2×2
Filter size
9 × 9 × 32
Maxout2
Layer 2
Max pooling
2×2
Filter size
9 × 9 × 64
2
Layer 3 Maxout
Max pooling
2×2
Layer 4 Sigmoid 200(Dropout:50%)
Output17
Input

4

Experiments

We evaluate the proposed method by comparing its performance with those of
the CNN for a single task and conventional heterogeneous learning. In theseexperiments, we perform facial point detection, gender recognition, race recognition, age estimation and smile ratio estimation. For facial point detection, we
use regression estimation to detect five facial points : the left eye, right eye, nose,left mouth, and right mouth, Smile ratio estimation is identified as regressionof the value between 0 and 99. Note that, smile ratio label is the average ofsome smile ratios that some people are given as labels. Age label is identified as
regression of the value between 0 and 66. Race recognition is identified as Asian,White, or Black.
We employ a CNN that consists of three convolutional layers and three fully
connectied layers, as shown in Tab. 1. In training of single task CNN, convolution layers and fully connected layers are have the same structure. In contrast,
number of units in the output layer is equal to the number of classes for eachfacial attribute task. The total number of iterations to update the parameters
is 1,000,000, the training coeﬃcient η is set to 0.001, and the mini-batch sizeis 10. The comparison dataset consists of 53,663 facial images that were captured by aggregating face images from the Web. However, almost no publisheddataset has been given any facial attribute labels, because we created a facial
attribute dataset that has been given five facial attribute. Note that the training
sample consists of 42,663 images and the test sample consists of 11,000 images.
The input images are 100×100 grayscale. We will publish this facial attributedataset as soon as it is ready. The evaluation method of facial point detectionis the same as that of Dantone et al. [2]. In age and smile ratio estimation, we
judge estimation to be successful if the diﬀerence between output and label isconnoted by the threshold, which are ±5 years and 10%.

Authors Suppressed Due to Excessive Length㼀㼞㼍㼕㼚㼕㼚㼓㻌㻱㼞㼞㼛㼞

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Fig. 4. Comparison of the training error for proposed method

4.1

Comparison of training errors

Figure 4 shows the training error for each task for the proposed method. The
training error for conventional heterogeneous learning is diﬀerent from the convergence value for each task, and the training error varies suddenly for the recognition task, as shown in Fig. 1(a). Additionally, training errors of the proposed
method for each task are lower overall than those of conventional heterogeneous
learning. The proposed method has a unified training error for each task, andsuppresses the dispersion training error variation. To achieve this result, theproposed method is stably trained by weighting the error function.
4.2

Comparison of performance for the proposed method

In Fig. 5, we compare the performances of single task learning conventional
heterogeneous learning and the proposed method. The accuracy of the regression
tasks is lower for single task learning than conventional heterogeneous learning,
especially for the facial point detection task. This is because facial point detection
most negatively aﬀects other tasks training errors, such as the diﬀerence between
training error variation. Compared with conventional heterogeneous learning andthe proposed method, we improved performance by approximately 5% and theaccuracy of the facial point detection task by approximately 14%. This meansthat the proposed method is stably able to train by extracting available facial
features.
Figure 6 shows an example of facial image analysis using conventional heterogeneous learning and the proposed method. The first and third columns show
result of examples of conventional heterogeneous learning, and the second andfourth columns show results of the proposed method. Additionally, the left images are the input image and result of facial point detection by the conventional

Title Suppressed Due to Excessive Length

9Fig. 5. Comparison of the proposed method and other method

heterogeneous learning or proposed method, and the text on their right is resultsof subtasks such as gender, age, race, and smile ratio. The green points are facial points detected by conventional heterogeneous learning or proposed method.
The red text is inaccurate recognition or estimation. As shown in Fig. 6, we observe that the proposed method is robust to faces with large pose variation,lighting, and severe occlusion. Additionally, the processing time of our method
is approximately 22ms to analyze one image on an Intel Core i7-4790 (3.4GHz)
with 8GB of memory, and the processing time is approximately 1.8 ms to analyze
one image on GeForce GTX980.

5

Disscusion

In this section, we define the eﬀectiveness of the proposed method by comparing various viewpoints of conventional heterogeneous learning and the proposed
method.
5.1

Performance of integrating training error functions

In conventional heterogeneous learning, mean square error function and crossentropy error function are employed as error functions of the regression andrecognition tasks, respectively. However, there are noticeable diﬀerences between
the error ranges of the mean squared error function and the cross entropy error

10Authors Suppressed Due to Excessive Length

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Fig. 6. Comparison of examples of facial image analysis

Title Suppressed Due to Excessive Length

11function. Thus, we integrate the error range from 0 to 1 by exchanging the crossentropy error function for the mean squared error function for recognition tasks.
In this section, we evaluate integrating the error range by changing training error
functions.
Figure 7 shows the experimental results of classification accuracy that integrates mean square error function or not. When we compare the accuracy ofregression tasks, proposed method is improved performance by approximately
20% . This mean that we can suppress the diﬀerence of error between regression
tasks and recognition tasks, and this way can improve the accuracy of regression tasks that are susceptible to aﬀect the training error of recognition tasks,
especially.

Fig. 7. Results of accuracy that integrates mean square error function or not

5.2

Regression tasks performance when threshold shifts

In experiments at section 4.2, we evaluate regression tasks that set to be fixed
threshold, and if the output of a regression task is over than the threshold, theoutput is correct. if the output of regression task is under than the threshold, theoutput is missing classification. Therefore, we evaluate the accuracy of regression
tasks by shifting the threshold for each method.
Figure 8 shows classification accuracy that shifts the threshold between 5 to
20 for facial point detection. If we compare the CNN of single task, proposed
method is less performance than CNN of single task. However, If we compare
the heterogeneous learning, proposed method is significantly better performance

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Fig. 8. Classification accuracy of facial point detection

Fig. 9. Classification accuracy of smile ratio and age estimations

than conventional heterogeneous learning. Figure 9(a) and Fig. 9(b) show classification accuracy that shifts the threshold between 5 to 20 for smile ratio estimation and age estimation, respectively. Proposed method is better performance
than conventional heterogeneous learning and CNN of single task in age estimation. Improving the performance of age estimations was caused by improving thefacial point detection indebted proposed method. When CNN is trained with facial position by using heterogeneous learning, CNN easily focuses on facial part,and improving the performance by getting features that eﬀectual estimate.
5.3

Visualization weight filters and feature maps

Weight filters and feature maps of CNN with heterogeneous learning are visualized in Figure 10. Note that, we visualize them in the first layer. Figure 10(a)shows visualization of weight filters and feature maps of conventional heterogeneous learning, and Fig. 10(b) shows visualization of weight filters and featuremaps of the proposed method. Weight filters of conventional heterogeneous learning are shown a clear contrast between light and shape, as shown in Fig. 10(a).
However, conventional heterogeneous learning was outputted weak response at
facial part such as eye and mouth in feature maps. On the other hand, weightTitle Suppressed Due to Excessive Length

Weight ﬁlters at 1st Conv.

Input image

Feature maps(a) Heterogeneous Learning

Weight ﬁlters at 1st Conv.

Input image

Feature maps(b) Weighted Heterogeneous Learning

Fig. 10. Visualization weight filters and feature maps

13

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Authors Suppressed Due to Excessive Length

filters of proposed method were noisy, nevertheless, proposed method was outputted strong response at facial part such as eye and mouth in feature maps, as
shown in Fig. 10(b).

6Conclusion

In this paper, we proposed a method to improve the performance of heterogeneous learning for facial image analysis. As a result, compared with conventional
heterogeneous learning, the proposed method improved performance by approximately 5% and the accuracy of the facial point detection task by approximately
14%.

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