Age group identification
using gaze-guided feature extraction
Michiko Inoue

Masashi Nishiyama

Yoshio Iwai

Graduate School of Engineering
Tottori University, Japan.
mi.inoue@tottori-u.ac.jp

Graduate School of Engineering
Tottori University, Japan.
nishiyama@tottori-u.ac.jp

Graduate School of Engineering
Tottori University, Japan.
iwai@tottori-u.ac.jp

Abstract—When an observer identifies the age group of a
subject in an image, the observer’s gaze focuses on a region
containing an informative feature. Our aim is to improve theaccuracy of age group identification by extracting features fromthe regions where an observer’s gaze converges. Existing studies
have analysed observer gaze on facial images, but not on images
containing the subject’s whole body. Here, we analysed which
regions of the whole-body image an observer’s gaze focusedon while the observer performed this task. The experimental
results revealed that an observer’s gaze is drawn to the headof the subject regardless of the subject’s age group. They also
revealed that the gaze-guided feature extraction in deep learningand machine learning improved the accuracy of age group
identification.
Index Terms—Age group, Identification, Observer, Gaze distribution

I. I NTRODUCTION
There is a demand for a technique to identify the agegroups of people from images acquired from cameras installedin public spaces, e.g., shopping centres and airports. This
technique is expected to be used to collect the age groupsof people present in public spaces and perform appropriate
marketing for the individuals belonging to each age group.
Deep learning and machine learning are commonly used toidentify the age group of an individual from images with highaccuracy [1]. To further improve the accuracy of age group
identification, we consider introducing the gaze distribution
measured when human observers identify an age group into
the feature extraction of deep learning and machine learningtechniques. We assume that the image regions of interest tothese observers contain informative features for age group
identification, even if they are used for feature extraction in
deep learning and machine learning.
Recently, it has been reported that feature extraction techniques based on gaze distribution [2], [3] are effective inseveral image recognition tasks. Murrugarra-Llerena et al. [2]analysed observer gaze distributions by giving observers thetask of predicting the impression of a subject’s facial appearance in an image. They also improved the prediction accuracyof facial impressions by introducing the gaze distribution into
the feature extraction of machine learning. Nishiyama et al. [3]analysed observer gaze distributions by giving observers thetask of gender recognition for images containing the wholebodies of people. They also improved the accuracy of genderrecognition by introducing these gaze distributions into thefeature extraction of deep learning. However, these existingstudies [2], [3] did not analyse observer gaze distributions forthe age group identification task. They also were not evaluatedfor the case where the gaze distribution was used to extract
features for age group identification.
To analyse observer gaze distributions in the age group
identification task, Daksha et al. [4] revealed the facial parts
that observers primarily view when given the task to identify
the age group from facial images. Specifically, they reported
that observers look at the brow and nose of a face whenidentifying age. However, they did not analyse the case inwhich whole-body images of people were used as the stimulus.
Thus, it is difficult to use the findings of Daksha et al. [4]directly for age group identification in images of people. Theyalso did not consider using observer gaze distributions forfeature extraction in age group identification.
Here, we measure and analyse observer gaze distributions
by giving observers the task of identifying the age groupsof individuals in images containing whole bodies. We alsoevaluate whether the accuracy of age group identification canbe improved by introducing the gaze distributions into feature
extraction in deep learning and machine learning. From theexperimental results, we found that observers performing thistask focused their gaze on the subject’s head. In addition,
we suggest that there is the possibility of improving theaccuracy of age group identification using gaze-guided feature
extraction.
II. O BSERVER GAZE DISTRIBUTIONS IN THE AGE GROUPIDENTIFICATION TASK

A. Age groups used in our analysis
We consider the age group identification task given toobservers to measure gaze distributions. Generally, age groups
are set using various age ranges for different applications. Forexample, the major age groups in Japanese marketing are theF1 layer (20–34 years old) and F2 layer (35–49 years old).
In this paper, we use public datasets with age group labels
assigned to the people in the images. We must pay attention
to the fact that the age ranges for determining the age groups
vary from one dataset to another. Below, we describe theage group labels for two representative datasets: the CUHK
dataset, which is part of the PETA dataset [5], and the RAPCUHKRAP(a) 𝐴1 (17-30 years old)(a) CUHK(b) RAP

Fig. 2. Average images of the stimulus images in the CUHK and RAP
datasets.

Observer110–120 cm
65 cm

CUHK

RAP
(b) 𝐴2 (31-45 years old)

Fig. 1. Examples of the stimulus images.

Display
100 cm
Eye tracker
73.5 cm

Fig. 3. Experimental setting for gaze measurement.

dataset [6]. Furthermore, we must pay attention to the factthat there is a large bias in the number of images belonging
to each age group label in these datasets. When analysing thegaze distribution and evaluating identification accuracy, it isconsidered advisable to avoid an extremely small number ofimages of people in a particular age group. Therefore, for ourgaze analysis, we used the following two age group labels,
which have a large number of images of people belonging toeach age group:
A1 : Young age group (17–30 years old),A2 : Middle age group (31–45 years old).
In the CUHK dataset, the age group labels ‘Under 30’,
‘Under 45’, ‘Under 60’, and ‘Over 60’ were assigned. Ofthese, the number of ‘Under 30’ and ‘Under 45’ images was2572 out of 2720.1 In contrast, the number of ‘Under 60’ and‘Over 60’ images was 148, less than 10% of the total. Thus,
we assigned the ‘Less than 30’ images to A1 and ‘Less than45’ images to A2 . By contrast, in the RAP dataset, the agegroup labels ‘Age under 16’, ‘Age 17–30’, ‘Age 31–45’, and‘Age over 46’ were assigned. Here, we used the images fromthe camera conditions ‘CAM01’ and ‘CAM25’. The numberof ‘Age 17–30’ and ‘Age 31–45’ images was 4670 out of 4925.
In contrast, the number of ‘Age under 16’ and ‘Age over 46’
images was 255, which is less than 10% of the total. Thus, weassigned ‘Age 17–30’ images to A1 and ‘Age 31–45’ images
to A2 . Note that the age boundaries, such as exactly 30 years
old, are handled differently in each dataset, but we used theabove age group labels.
B. Stimulus images
We describe the stimulus images used to measure thegaze distribution in age group identification. Figure 1 shows
1 We did not allow the same subject to appear in the images. Attribute labelsin the RAP v1 dataset were used to identify images that could be of the samesubject.

examples of the stimulus images. To control for experimental
conditions, the numbers of male and female subjects in thestimulus images were kept equal. The proportions of thesubject body orientations (front, back, left, and right) in theimages are the same. The same subject appeared only once inall stimulus images. The number of stimulus images was 32
for each dataset.
We assumed that the positions of the subject’s body partsin the stimulus image are almost aligned. To confirm thisalignment, we averaged the 32 stimulus images in each dataset.
The results are shown in Figs. 2(a) and (b). In both figures,a black ellipse appears at the top of the average image. Thisellipse corresponds to the head of the subjects. A large black
area appears near the centre of the average image. This area
corresponds to the torso of the subjects. In addition, a light
black area appeared at the bottom of the average image. Thisarea corresponds to the feet of the subjects.
C. Experimental conditions for gaze measurement
Sixteen observers (10 men and 6 women, 22.4±1.0 yearsold) participated. Figure 3 shows the experimental setting forgaze measurement. The size of the display was 24 inches(resolution: 1920×1080 pixels). An eye tracker (Gaze PointGP3) was used to measure the gaze distribution. The sampling
rate of the eye tracker was 60 Hz. The stimulus image wasenlarged to 960 pixels in height while keeping the aspect ratio
fixed. We showed the enlarged stimulus image to the observeron the display.
In the age group identification task given to the observers,
we asked the following question:
Q : Which age group label, A1 or A2 , do you identify thesubject in the image as belonging to?
The procedure for asking this question to the observers wasas follows.

𝐴1

𝐴2

𝐴1

(a) CUHK

𝐴2(b) RAP

Front

Left
Back
(a) CUHK

Right

Front

Back

Left

Right

Fig. 4. Gaze distribution measured using stimulus images for each dataset inthe task of identifying age group labels A1 and A2 .

P1 : We selected one observer randomly and asked questionQ.
P2 : The grey image was displayed for 2 seconds.
P3 : We selected one stimulus image randomly and displayed that for 2 seconds.
P4 : The observer verbally responded with the subject’s age
group label while a black image was displayed for 3seconds.
P5 : Procedures P2 to P4 were repeated for each stimulus
image.
P6 : Procedures P1 to P5 were repeated until all observers
were finished.
D. Generation of gaze distribution
We describe the procedure for generating the gaze distribution. We used the observer’s gaze positions measured inprocedure P3 , and the method described in [3] was performed.
Specifically, the gaze positions measured for a period of
2 seconds were accumulated in the time direction at thecorresponding positions in the gaze distribution image. Whenaccumulating these points, we used only the gaze positions thatthe eye tracker outputted as fixations. Note that an observer
not only looks at the gaze position on the stimulus image ateach time point, but also the region surrounding this position.
Thus, we used a Gaussian kernel to filter the gaze distribution
image during the accumulation. In addition, for all observers
and all stimulus images, the gaze positions were accumulated
at the corresponding positions in the gaze distribution image.
This gaze distribution image was generated for each age groupin each dataset.
E. Results of gaze distribution analysis
Figure 4(a) shows the gaze distribution measured during
the task of identifying age group labels A1 and A2 using
the stimulus images from the CUHK dataset. In the gaze
distribution, the black areas indicates the points at which theobserver gaze positions gathered. Moreover, white areas indicate areas where observer gaze positions were not gathered.
To investigate which body part is the primary focus of theobservers, we compared the gaze distribution in Fig. 4(a) withthe body silhouette in the average image in Fig. 2(a). For
both age group labels A1 and A2 , we found that the observers
focused on the subjects’ heads in the stimulus images. By(b) RAP
Fig. 5. Gaze distribution generated for each subject’s body orientation.

contrast, the observers did not focus near the subjects’ feet.
Next, Fig. 4(b) shows the gaze distribution measured during
the task of identifying A1 and A2 using stimulus images fromthe RAP dataset. The gaze distribution in Fig. 4(b) is comparedwith the body silhouette in the average image in Fig. 2(b). We
again found that the observers focused on the subjects’ heads
and not near the subjects’ feet. The results in Fig. 4(b) for theRAP dataset show the same trend as the results in Fig. 4(a)for the CUHK dataset. The above results show that observers,when identifying age group labels, focus on the subject’s headfor both age group labels A1 and A2 .
We quantitatively evaluated how similar the gaze distribution for age group label A1 was to the gaze distribution for A2for each dataset. Specifically, Pearson’s correlation coefficientswere calculated for all gaze distributions. The results showthat the correlation coefficient between the A1 and A2 gaze
distributions was 0.91 for the CUHK dataset and 0.92 for theRAP dataset. Thus, we confirmed that the gaze distribution isvery similar regardless of the subject’s age group.
Furthermore, we investigated whether the gaze distribution
is affected by the subject’s body orientation in the stimulus
images. Figure 5 shows the gaze distribution generated foreach subject’s body orientation (front, back, left, or right). We
show the gaze distributions on the CUHK dataset in Fig. 5(a)and those on the RAP dataset in Fig. 5(b). Pearson’s correlation coefficient was calculated for the gaze distributions of all
subject body orientations. The average correlation coefficientof 4 C2 = 6 orientation combinations in the CUHK dataset
was 0.92±0.03, and that in the RAP dataset was 0.84±0.03.
We found that the orientation of the subject’s body producesvery similar gaze distributions when the observers viewed thestimulus images in the CUHK dataset. We also found that it

produces reasonably similar gaze distributions when observers
viewed the stimulus images in the RAP dataset.
III. AGE GROUP IDENTIFICATION USING THE GAZEDISTRIBUTION

A. Gaze-guided feature extraction
We used a simple weighting technique to extract featuresfrom the training and test images for age group identification. Specifically, the method [3] was performed for gazeguided feature extraction. In advance, we generated a single
gaze distribution for each dataset by accumulating the gaze
distributions generated for age group labels A1 and A2 . We
assigned large weights to the regions of accumulated gaze
distributions where the observers frequently fixated and small
weights to those regions they rarely viewed. The pixel valuesof the training and test images were multiplied by the gaze
distribution weights pixel by pixel. Then, we examined thetraining and test images weighted by the gaze distribution
using deep learning and machine learning techniques.
B. Accuracy of age group identification
To evaluate the accuracy of age group identification, we set
the following comparison conditions:M1 : Without gaze-guided feature extraction,
M2 : With gaze-guided feature extraction.
We used the CUHK and RAP datasets described in Section II and performed 10-fold cross-validation. For the CUHK
dataset, each validation had 2286 training images and 254 testimages. For the RAP dataset, each validation had 3317 training
images and 1321 test images. Note that the training images
were completely separate from the test images.
We used representative deep learning and machine learningtechniques, a convolutional neural network (CNN), a gradientboosting decision tree (GB), a kernel support vector machine(SVM), and logistic regression (LR), for age group identification. In the CNN, we used a shallow network model [7], which
consists of two convolution layers and two pooling layers. Inthe LR, we set the inverse of regularization strength C = 1.
In the GB, we set the number of decision trees to 1000 andthe maximum depth of nodes to 3. In the SVM, we used theradial basis function kernel and the regularization parameter
C = 1.
Table I shows the accuracy of age group identification withand without gaze-guided feature extraction. We see that theaccuracy of M2 is better than that of M1 for the deep learningand machine learning techniques on the CUHK dataset. We
also see the same trend in the RAP dataset. Thus, we believethat the gaze-guided feature extraction improves the accuracyof age group identification.
IV. C ONCLUSIONSWe analysed observer gaze distributions, measured when
they perform the task of identifying the age group ofindividuals in images containing whole bodies. In addition,
we evaluated whether the use of the gaze distribution improves
the accuracy of age group identification. To measure the gaze

TABLE I
ACCURACY OF AGE GROUP IDENTIFICATION WITH AND WITHOUTGAZE - GUIDED FEATURE EXTRACTION .
Dataset
CUHK

RAP

Technique
CNN
GB
SVMLR
CNN
GB
SVM
LR

M1 : w/o gaze
0.57
0.58
0.60
0.57
0.57
0.63
0.63
0.62

M2 : w/ gaze
0.59
0.60
0.63
0.59
0.61
0.64
0.65
0.63

distribution, we designed a task in which observers viewed
full-body images of people as stimuli and were asked toidentify the age group labels. Our gaze analysis revealed thatobservers identifying the subject’s age group primarily viewed
the subject’s head. We suggested that there is the possibilityof improving the accuracy of age group identification by using
the gaze distribution for feature extraction. In future work,
we will expand our research to investigate more detailed age
group labels and analyse the gaze distribution using highresolution datasets.
Acknowledgment
This work was partially supported by JSPS KAKENHI Grant
No. JP23K11145.
R EFERENCES[1] X. Wang, S. Zheng, R. Yang, A. Zheng, Z. Chen, J. Tang, and B. Luo,
“Pedestrian attribute recognition: A survey,” Pattern Recognition, vol.
121, pp. 108220, 2022.
[2] N. Murrugarra-Llerena and A. Kovashka, “Learning attributes fromhuman gaze,” Proceedings of IEEE Winter Conference on Applicationsof Computer Vision, pp. 510–519, 2017.
[3] M. Nishiyama, R. Matsumoto, H. Yoshimura, and Y. Iwai, “Extractingdiscriminative features using task-oriented gaze maps measured fromobservers for personal attribute classification,” Pattern Recognition
Letters, vol. 112, pp. 241–248, 2018.
[4] D. Yadav, N. Kohli, E. Kalsi, M. Vatsa, R. Singh, and A. Noore,“Unraveling human perception of facial aging using eye gaze,” inProceedings of IEEE/CVF Conference on Computer Vision and PatternRecognition Workshops, 2018, pp. 2221–22217.
[5] Y. Deng, P. Luo, C. C. Loy, and X. Tang, “Pedestrian attribute recognition
at far distance,” ACM, pp. 789–792, 2014.
[6] D. Li, Z. Zhang, X. Chen, H. Ling, and K. Huang, “A richly annotateddataset for pedestrian attribute recognition,” CoRR, abs/1603.07054,
2016.
[7] A. Grigory, B. Sid-Ahmed, R. Natacha, and D. Jean-Luc, “Learned vs.
hand-crafted features for pedestrian gender recognition,” in Proceedingsof 23rd ACM International Conference on Multimedia, 2015, pp. 1263–1266.