• Korean Journal of Artificial Intelligence
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• v.11 no.1
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• pp.9-15
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• 2023

As mobile devices such as smartphones, tablets, and kiosks become increasingly prevalent, there is growing interest in developing alternative input systems in addition to traditional tools such as keyboards and mouses. Many people use their own bodies as a pointer to enter simple information on a mobile device. However, methods using the body have limitations due to psychological factors that make the contact method unstable, especially during a pandemic, and the risk of shoulder surfing attacks. To overcome these limitations, we propose a simple information input system that utilizes gaze-tracking technology to input passwords and control web surfing using only non-contact gaze. Our proposed system is designed to recognize information input when the user stares at a specific location on the screen in real-time, using intelligent gaze-tracking technology. We present an analysis of the relationship between the gaze input box, gaze time, and average input time, and report experimental results on the effects of varying the size of the gaze input box and gaze time required to achieve 100% accuracy in inputting information. Through this paper, we demonstrate the effectiveness of our system in mitigating the challenges of contact-based input methods, and providing a non-contact alternative that is both secure and convenient.

• Fashion & Textile Research Journal
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• v.23 no.3
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• pp.323-336
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• 2021

This study investigated the cognitive process of fashion design images through eye activity tracking. Differences in the cognitive process and gaze activity according to image elements were confirmed. The results of the study are as follows. First, a difference was found between groups in the gaze time for each section according to the model and design. Although model diversity is an important factor leading the interest of observers, the simplicity of the model was deemed more effective for observing the design. Second, the examination of the differences by segments regarding the gaze weight of the image area showed differences for each group. When a similar type of model is repeated, the proportion of face recognition decreases, and the proportion of design recognition time increases. Conversely, when the model diversity is high, the same amount of time is devoted to recognizing the model's face in all the processes. Additionally, there was a difference in the gaze activity in recognizing the same design according to the type of model. These results enabled the confirmation of the importance of the model as an image recognition factor in fashion design. In the fashion industry, it is important to find a cognitive factor that attracts and retains consumers' attention. If the design recognition effect is further maximized by finding service points to be utilized, the brand's sustainability is expected to be enhanced even in the rapidly changing fashion industry.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.249-252
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• 2000

An experimental multimodal system combining natural input modes such as speech, lip movement, and gaze is proposed in this paper. It benefits from novel human-compute. interaction (HCI) modalities and from multimodal integration for tackling the problem of the HCI bottleneck. This system allows the user to select menu items on the screen by employing speech recognition, lip reading, and gaze tracking components in parallel. Face tracking is a supplementary component to gaze tracking and lip movement analysis. These key components are reviewed and preliminary results are shown with multimodal integration and user testing on the prototype system. It is noteworthy that the system equipped with gaze tracking and lip reading is very effective in noisy environment, where the speech recognition rate is low, moreover, not stable. Our long term interest is to build a user interface embedded in a commercial car navigation system (CNS).

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.103-110
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• 2011

In this paper, we propose a gaze recognition interface for smart wheelchair. The gaze recognition interface is a user interface which recognize the commands using the gaze recognition and avoid the detected obstacles by sensing the distance through range sensors on the way to driving. Smart wheelchair is composed of gaze recognition and tracking module, user interface module, obstacle detector, motor control module, and range sensor module. The interface in this paper uses a camera with built-in infra red filter and 2 LED light sources to see what direction the pupils turn to and can send command codes to control the system, thus it doesn't need any correction process per each person. The results of the experiment showed that the proposed interface can control the system exactly by recognizing user's gaze direction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.191-197
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• 2015

In this paper, we propose the system which recognize the gaze using Random Forests in vehicular environment based on smart-phone. Proposed system is mainly composed of the following: face detection using Adaboost, face component estimation using Histograms, and gaze recognition based on Random Forests. We detect a driver based on the image information with a smart-phone camera, and the face component of driver is estimated. Next, we extract the feature vectors from the estimated face component and recognize gaze direction using Random Forest recognition algorithm. Also, we collected gaze database including a variety gaze direction in real environments for the experiment. In the experiment result, the face detection rate and the gaze recognition rate showed 82.02% and 84.77% average accuracies, respectively.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.53-60
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• 2018

This paper describes the experiments of gaze tracking utilizing a low-cost electrooculogram measuring device. The goal of the experiments is to verify whether the low-cost device can be used for a complicated human-computer interaction tool, such as the eye-writing. Two experiments are conducted for this goal: a simple gaze tracking of four directional eye-movements, and eye-writing-which is to draw letters or shapes in a virtual space. Eye-written alphabets were obtained by two PSL-iEOGs and an Arduino Uno; they were classified by dynamic positional warping after preprocessed by a wavelet function. The results show that the expected recognition accuracy of the four-directional recognition is close to 90% when noises are controlled, and the similar median accuracy (90.00%) was achieved for the eye-writing when the number of writing patterns are limited to five. In future works, additional algorithms for stabilizing the signal need to be developed.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.266-272
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• 2013

Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition. In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".

• (The) Korean Journal of Advertising
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• v.29 no.7
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• pp.29-53
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• 2018

In our study, we investigated the effects of position of endorser and endorser's gaze direction(direct/averted_image/averted_text) on advertising attitude, purchase intent and brand recognition using eye-tracking method. Focusing on the printed cosmetic ads which the role of endorser is important and indirect persuade route is relatively is emphasized, we conducted experiment on 36 participants in 20s. As prior studies, our results shows that participants paid attention to more and faster on specific element which the endorser is gazing at. But it was not reflected to ad attitude and purchase intent directly. When the endorser is positioned in left the side, the highest purchase intent was shown in direct gaze condition, while when the endorser is on the right side, the highest ad attitude was shown in gazing image condition. Additionally, the brand recognition task following eye-tracking experiment shows that recognition accuracy was higher only in condition which the endorser is in the left side looking at the product image. These results demonstrated that the gaze direction of endorser plays a role as attentional guidance, which means it can lead customer's attention to particular region in the printed ad, but the effect can be varied depending on the position of endorser and which type of information the endorser is gazing at. Therefore, ultimately, to increase customer's ad attitude and purchase intent, complex consideration of not only the gazing direction of the endorser, but the position of endorser and other diverse elements is necessary.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.229-235
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• 2011

Objective: Making a new ergonomic interface system based on camera vision system, which helps the handicapped in home environment. Background: Enabling the handicapped to manipulate the consumer electronics by the proposed interface system. Method: A wearable device for capturing the eye image using a near-infrared(NIR) camera and illuminators is proposed for tracking eye gaze position(Heo et al., 2011). A frontal viewing camera is attached to the wearable device, which can recognize the consumer electronics to be controlled(Heo et al., 2011). And the amount of user's eye fatigue can be measured based on eye blink rate, and in case that the user's fatigue exceeds in the predetermined level, the proposed system can automatically change the mode of gaze based interface into that of manual selection. Results: The experimental results showed that the gaze estimation error of the proposed method was 1.98 degrees with the successful recognition of the object by the frontal viewing camera(Heo et al., 2011). Conclusion: We made a new ergonomic interface system based on gaze tracking and object recognition Application: The proposed system can be used for helping the handicapped in home environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.756-760
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• 2010

This paper is proposed to an novel facial behavior recognition system for driver's fatigue detection. Facial behavior is shown in various facial feature such as head expression, head pose, gaze, wrinkles. But it is very difficult to clearly discriminate a certain behavior by the obtained facial feature. Because, the behavior of a person is complicated and the face representing behavior is vague in providing enough information. The proposed system for facial behavior recognition first performs detection facial feature such as eye tracking, facial feature tracking, furrow detection, head orientation estimation, head motion detection and indicates the obtained feature by AU of FACS. On the basis of the obtained AU, it infers probability each state occur through Bayesian network.