• Journal of Digital Convergence
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• v.13 no.6
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• pp.151-156
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• 2015

In this paper, we have proposed personal multimodal biometric authentication system based on face detection, recognition and speaker verification for smart-phone environment. Proposed system detect the face with Modified Census Transform algorithm then find the eye position in the face by using gabor filter and k-means algorithm. Perform preprocessing on the detected face and eye position, then we recognize with Linear Discriminant Analysis algorithm. Afterward in speaker verification process, we extract the feature from the end point of the speech data and Mel Frequency Cepstral Coefficient. We verified the speaker through Dynamic Time Warping algorithm because the speech feature changes in real-time. The proposed multimodal biometric system is to fuse the face and speech feature (to optimize the internal operation by integer representation) for smart-phone based real-time face detection, recognition and speaker verification. As mentioned the multimodal biometric system could form the reliable system by estimating the reasonable performance.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.373-383
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• 2016

With mobile-epoch and emerging of Fin-tech, Bio-recognition technology utilizing bio-information in secure method has spread. Specially, In order to change convenient payment services and transportation cards, the combination of biometrics and mobile services are being expanded. The basic concept of authentication such as access control, IA&A, OpenID, OAuth 1.0a, SSO, and Biometrics techniques are investigated, and the protocol stack for security API platform, FIDO, SCIM, OAuth 2.0, JSON Identity Suite, Keystone of OpenStack, Cloud-based SSO, and AIM Agent are described detailed in aspect of application of AIM. The authentication technology in domestic and foreign will accelerate technology development and research of standardization centered in the federated FIDO Universal Authentication Framework(UAF) and Universal 2 Factor Framework(U2F). To accommodate the changing needs of the social computing paradigm recently in this paper, the trends of various authentication technology, and design and function of AIM framework was defined.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1485-1490
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user, does face recognition and vital for many surveillance based systems. Advantage of face recognition when compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to decrease in dimension from of image acquisition step and various changes associated with face pose and background. Factors that deteriorate performance of face recognition are many such as distance from camera to face, lighting change, pose change, and change of facial expression. In this paper, we implement a new 3D active camera system to prevent various pose variation that influence face recognition performance and propose face recognition algorithm for intelligent surveillance system and mobile robot system.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1815-1821
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• 2015

Rapid increase in population growth has made the mankind to delve in appropriate identification of individuals through biometrics. Foot Print Recognition System is a new challenging area involved in the Personal recognition that is easy to capture and distinctive. Foot Print has its own dimensions, different in many ways and can be distinguished from one another. The main objective is to provide a novel efficient automated system Segmentation using Foot Print based on structural relations among the features in order to overcome the existing manual method. This system comprises of various statistical computations of various foot print parameters for identifying the factors like Instep-Foot Index, Ball-Foot Index, Heel- Index, Toe- Index etc. The input is naked footprint and the output result to an efficient segmentation system thereby leading to time complexity.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.43-45
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• 2012

This paper is based on embedding the biometrics techniques on GPU for better computational efficiency and fast matching process using the parallel nature of the GPU processors to compare thousands of images for fingerprint recognition in a fraction of a second. In this paper we worked on GPU (INVIDIA GeForce GTX 260 with compute capability 1.3 and dual core-2-dou processor) for fingerprint matching and found that the efficiency is better than the results with related work already done on CMOS, CPU, ARM9, MATLAB Neural Networks etc which shows the better performance of our system in terms of computational time. The features matching process proposed for fingerprint recognition and the verification procedure is done on 5,000 images which are available online in the databases FVC2000, 2002, 2004 [1].

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.666-672
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• 2016

Recently the fingerprint is widely used as one of biometrics to improve the security of financial mobile applications, because of its user convenience and high recognition rate. However, in order to apply fingerprint algorithms to finance and security applications, the recognition rate and processing speed of the fingerprint algorithms have to be improved further. In this paper, we propose the parallel fingerprint enhancement algorithm on general-purpose computing on graphics processing unit (GPGPU) using OpenCL. We discuss the analysis of the parallelism in the fingerprint algorithm as well as the exploration of optimization parameters of the parallel fingerprint algorithm to improve the performance. The experimental results showed that the execution of parallel fingerprint enhancement algorithm on GPGPUs was accelerated from 29.4 upto 69.2 times compared with the execution of the original one on the host CPUs.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.925-927
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• 2005

홍채 인식(Iris Recognition)은 동공과 흰자위 사이에 있는 홍채의 모양 패턴이 평생 변하지 않고, 사람마다 다른 패턴을 가진다는 특성을 이용하여 개인을 식별하는 기술로, 생체인식(Biometrics) 부분에서는 탁월한 식별력 및 신뢰성을 인정 받고 있다. 상당수의 기존 연구들은 원천 특허를 채택한 상태에서 성능 개선을 연구해 왔기 때문에 원천적인 한계를 가지고 있었다. 본 논문은 웨이블릿(Wavelet) 변환을 이용하여 특징을 추출하는 기존 방식과 다르게 프랙탈(Fractal) 방법으로 압축된 다수의 원 영상에 대해 입력된 영상의 유사도를 측정, 개인을 식별하는 새로운 홍채인식 방법을 제안한다. 이를 통해 타 연구들에서 제안했던 특별한 최적화 알고리즘을 사용하지 않고도 크게 떨어지지 않는 인식률을 얻을 수 있다.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.211-212
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• 2007

Pose-variation factors present a significant problem in 2D face recognition. To solve this problem, there are various approaches for a 3D face acquisition system which was able to generate multi-view images. However, this created another pose estimation problem in terms of normalizing the 3D face data. This paper presents a 3D head pose-normalization method using 2D and 3D interaction. The proposed method uses 2D information with the AAM(Active Appearance Model) and 3D information with a 3D normal vector. In order to verify the performance of the proposed method, we designed an experiment using 2.5D face recognition. Experimental results showed that the proposed method is robust against pose variation.

• Smart Media Journal
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• v.7 no.4
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• pp.61-69
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• 2018

Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.493-494
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• 2022

The development of livestock faces intensive farming results in a rising need for recognition of individual animals such as cows and pigs is related to high traceability. In this paper, we present a non-invasive biometrics systematic approach based on the deep-learning classification model to pig face identification. Firstly, in our systematic method, we build a ROS data collection system block to collect 10 pig face data images. Secondly, we proposed a preprocessing block in that we utilize the SSIM method to filter some images of collected images that have high similarity. Thirdly, we employ the improved image classification model of CNN (ViT), which uses the finetuning and pretraining technique to recognize the individual pig face. Finally, our proposed method achieves the accuracy about 98.66%.