• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.4
• /
• pp.174-179
• /
• 2018

Currently, various security technologies such as fingerprint recognition and face recognition are being developed. However, although many technologies have been developed, the field of incorporating technologies is quite limited. In particular, it is easy to adapt modern security technologies into existing digital systems, but it is difficult to introduce new digital technologies in systems using analog systems. However, if the system can be widely used, it is worth replacing the analog system with the digital system. Therefore, the selected topic is the electronic attendance system. In this paper, a camera is installed to a door to perform a Haar-like feature training for face detecting and real-time face recognition with a Eigenface in principal component analysis(PCA) based face recognition using raspberry pi. The collected data was transmitted to the smartphone using wireless communication, and the application for the viewer who can receive and manage the information on the smartphone was completed.

• Journal of IKEEE
• /
• v.23 no.2
• /
• pp.642-651
• /
• 2019

One of the most important research topics on intelligent transportation systems in recent years is detecting and recognizing a license plate. The license plate has a unique identification data on vehicle information. The existing vehicle traffic control system is based on a stop and uses a loop coil as a method of vehicle entrance/exit recognition. The method has the disadvantage of causing traffic jams and rising maintenance costs. We propose to exploit differential image of camera background instead of loop coil as an entrance/exit recognition method of vehicles. After entrance/exit recognition, we detect the candidate images of license plate using the morphological characteristics. The license plate can finally be detected using SVM(Support Vector Machine). Letter and numbers of the detected license plate are recognized using CNN(Convolutional Neural Network). The experimental results show that the proposed algorithm has a higher recognition rate than the existing license plate recognition algorithm.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.3
• /
• pp.151-158
• /
• 2019

Patients with disabilities from various reasons such as disasters, injuries or chronic illness or elderly with limited body motion range due to aging are recommended to participate in rehabilitation programs at hospitals. But typically, it's not as simple for them to commute without help as they have limited access outside of the home. Also, regarding the perspectives of hospitals, having to maintain the workforce and have them take care of the rehabilitation sessions leads them to more expenses in cost aspects. For those reasons, in this paper, a home-based remote rehabilitation system using motion recognition is developed without needing help from others. This system can be executed by a personal computer and a stereo camera at home, the real-time user motion status is monitored using motion recognition feature. The system tracks the joint range of motion(Joint ROM) of particular body parts of users to check the body function improvement. For demonstration, total of 4 subjects with various ages and health conditions participated in this project. Their motion data were collected during all 3 exercise sessions, and each session was repeated 9 times per person and was compared in the results.

• Journal of Engineering Education Research
• /
• v.23 no.2
• /
• pp.24-30
• /
• 2020

This paper proposes the approaches to the evaluation of learning using concepts of artificial intelligence. Among various techniques, deep learning algorithm is employed to achieve quantitative results of evaluation. In particular, this paper focuses on the process-based evaluation instead of the result-based one using face expression. The expression is simply acquired by digital camera that records face expression when students solve sample test problems. Face expressions are trained using convolutional neural network (CNN) model followed by classification of expression data into three categories, i.e., easy, neutral, difficult. To substantiate the proposed approach, the simulation results show promising results, and this work is expected to open opportunities for intelligent evaluation system in the future.

• Journal of Korea Society of Industrial Information Systems
• /
• v.9 no.2
• /
• pp.32-43
• /
• 2004

In this paper, a system which can effectively recognize the plate image extracted from camera set on a moving car is proposed. To extract car licence plate from moving vehicles, multiple candidates are maintained based on the strong vertical edges which are found in the region of car licence plate. A candidate region is selected among them based on the ratio of background and characters. We also make a comparative study of recognition performance between support vector machines and modular neural networks. The experimental results lead us to the conclusion that the former is superior to the latter. For a better recognition rate, a simple method combining the support vector machine with modular neural network where the output of the latter is used as the input of the former is suggested and evaluated. As we expected, the hybrid one shows the best result among those three methods we have mentioned.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.4
• /
• pp.34-40
• /
• 2008

This paper presents a vision based walking robot control system using hand shape recognition. To recognize hand shapes, the accurate hand boundary needs to be tracked in image obtained from moving camera. For this, we use an active contour model-based tracking approach with mean shift which reduces dependency of the active contour model to location of initial curve. The proposed system is composed of four modules: a hand detector, a hand tracker, a hand shape recognizer and a robot controller. The hand detector detects a skin color region, which has a specific shape, as hand in an image. Then, the hand tracking is performed using an active contour model with mean shift. Thereafter the hand shape recognition is performed using Hue moments. To assess the validity of the proposed system we tested the proposed system to a walking robot, RCB-1. The experimental results show the effectiveness of the proposed system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.9
• /
• pp.4491-4509
• /
• 2017

The present paper proposes a novel dynamic system for hand gesture recognition. The approach involved is comprised of three main steps: detection, tracking and recognition. First, the gesture contour captured by a 2D-camera is detected by combining the three-frame difference method and skin-color elliptic boundary model. Then, the trajectory of the hand gesture is extracted via a gesture-tracking algorithm based on an occlusion-direction oriented linear extrapolation predictor, where the gesture coordinate in next frame is predicted by the judgment of current occlusion direction. Finally, to overcome the interference of insignificant trajectory segments, the longest common subsequence (LCS) is employed with the aid of velocity information. Besides, to tackle the subgesture problem, i.e., some gestures may also be a part of others, the most probable gesture category is identified through comparison of the relative LCS length of each gesture, i.e., the proportion between the LCS length and the total length of each template, rather than the length of LCS for each gesture. The gesture dataset for system performance test contains digits ranged from 0 to 9, and experimental results demonstrate the robustness and effectiveness of the proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37C no.10
• /
• pp.925-932
• /
• 2012

In this paper, we develop a pattern recognition algorithm applied to a humanoid robot which is exploited as a guide for visually handicapped persons to find a desired path to their destinations. Behavior primitives of a humanoid robot are defined, and Canny's edge detection algorithm is employed to extract the pattern and color of the paving blocks that especially devised for visually handicapped persons. Based on these, an efficient path finding algorithm is developed and implemented on a humanoid robot, running on an embedded linux operating system equipped with a video camera. The performance of our algorithm is experimentally examined in terms of the response time and the pattern recognition ratio. In order to validate our algorithm in various realistic environments, the experiments are repeatedly performed by changing the tilt of paving blocks and the brightness in surrounding area. The results show that our algorithm performs sufficiently well to be exploited as a path finding system for visually handicapped persons.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.2
• /
• pp.135-140
• /
• 2012

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.2
• /
• pp.1-9
• /
• 2008

As the security requirements of mobile phones have been increasing, there have been extensive researches using one biometric feature (e.g., an iris, a fingerprint, or a face image) for authentication. Due to the limitation of uni-modal biometrics, we propose a method that combines face and iris images in order to improve accuracy in mobile environments. This paper presents four advantages and contributions over previous research. First, in order to capture both face and iris image at fast speed and simultaneously, we use a built-in conventional mega pixel camera in mobile phone, which is revised to capture the NIR (Near-InfraRed) face and iris image. Second, in order to increase the authentication accuracy of face and iris, we propose a score level fusion method based on SVM (Support Vector Machine). Third, to reduce the classification complexities of SVM and intra-variation of face and iris data, we normalize the input face and iris data, respectively. For face, a NIR illuminator and NIR passing filter on camera are used to reduce the illumination variance caused by environmental visible lighting and the consequent saturated region in face by the NIR illuminator is normalized by low processing logarithmic algorithm considering mobile phone. For iris, image transform into polar coordinate and iris code shifting are used for obtaining robust identification accuracy irrespective of image capturing condition. Fourth, to increase the processing speed on mobile phone, we use integer based face and iris authentication algorithms. Experimental results were tested with face and iris images by mega-pixel camera of mobile phone. It showed that the authentication accuracy using SVM was better than those of uni-modal (face or iris), SUM, MAX, NIN and weighted SUM rules.