• Title/Summary/Keyword: face recognition

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Affine Local Descriptors for Viewpoint Invariant Face Recognition

  • Gao, Yongbin;Lee, Hyo Jong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2014.04a
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    • pp.781-784
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    • 2014
  • Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we use Affine SIFT to detect affine invariant local descriptors for face recognition under large viewpoint change. Affine SIFT is an extension of SIFT algorithm. SIFT algorithm is scale and rotation invariant, which is powerful for small viewpoint changes in face recognition, but it fails when large viewpoint change exists. In our scheme, Affine SIFT is used for both gallery face and probe face, which generates a series of different viewpoints using affine transformation. Therefore, Affine SIFT allows viewpoint difference between gallery face and probe face. Experiment results show our framework achieves better recognition accuracy than SIFT algorithm on FERET database.

The Design and Implementation of a Performance Evaluation Tool for the Face Recognition System (얼굴인식시스템 성능평가 도구의 설계 및 구현)

  • Shin, Woo-Chang
    • Journal of Information Technology Services
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    • v.6 no.2
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    • pp.161-175
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    • 2007
  • Face recognition technology has lately attracted considerable attention because of its non-intrusiveness, usability and applicability. Related companies insist that their commercial products show the recognition rates more than 95% according to their self-testing. But, the rates cannot be admitted as official recognition rates. So, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of face recognition systems. In this paper, I propose a reference model for biometrics recognition evaluation tools, and implement an evaluation tool for the face recognition system based on the proposed reference model.

Using a Multi-Faced Technique SPFACS Video Object Design Analysis of The AAM Algorithm Applies Smile Detection (다면기법 SPFACS 영상객체를 이용한 AAM 알고리즘 적용 미소검출 설계 분석)

  • Choi, Byungkwan
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.11 no.3
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    • pp.99-112
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    • 2015
  • Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.

Face Recognition using Emotional Face Images and Fuzzy Fisherface (감정이 있는 얼굴영상과 퍼지 Fisherface를 이용한 얼굴인식)

  • Koh, Hyun-Joo;Chun, Myung-Geun;Paliwal, K.K.
    • Journal of Institute of Control, Robotics and Systems
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    • v.15 no.1
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    • pp.94-98
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    • 2009
  • In this paper, we deal with a face recognition method for the emotional face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images and have had a very difficult problem if we consider the facial expression. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated for the face recognition. And, this situation requires a robust face recognition algorithm then we use a fuzzy Fisher's Linear Discriminant (FLD) algorithm with the wavelet transform. The fuzzy Fisherface is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information. The experimental results obtained for the CBNU face databases reveal that the approach presented in this paper yields better recognition performance in comparison with the results obtained by other recognition methods.

A Study on Face Recognition on an UMPC (UMPC 환경에서의 얼굴인식 연구)

  • Nam, Gi-Pyo;Kang, Byung-Jun;Jeong, Dae-Sik;Park, Kang-Ryoung
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.831-832
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    • 2008
  • This paper proposes the experimental results and analysis of face recognition on an conventional UMPC(Ultra Mobile Personal Computer). With face images acquired by the embedded camera of UMPC, we detected the facial region by using Adaboost face detector. The detected image was normalized into a $32{\times}32$ pixel sized image for face recognition. We performed face recognition based on PCA (Principal Component Analysis). As experimental results, the TER (Total Error Rate) of face recognition was 19.77%.

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A Search Model Using Time Interval Variation to Identify Face Recognition Results

  • Choi, Yun-seok;Lee, Wan Yeon
    • International journal of advanced smart convergence
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    • v.11 no.3
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    • pp.64-71
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    • 2022
  • Various types of attendance management systems are being introduced in a remote working environment and research on using face recognition is in progress. To ensure accurate worker's attendance, a face recognition-based attendance management system must analyze every frame of video, but face recognition is a heavy task, the number of the task should be minimized without affecting accuracy. In this paper, we proposed a search model using time interval variation to minimize the number of face recognition task of recorded videos for attendance management system. The proposed model performs face recognition by changing the interval of the frame identification time when there is no change in the attendance status for a certain period. When a change in the face recognition status occurs, it moves in the reverse direction and performs frame checks to more accurate attendance time checking. The implementation of proposed model performed at least 4.5 times faster than all frame identification and showed at least 97% accuracy.

Sliding Active Camera-based Face Pose Compensation for Enhanced Face Recognition (얼굴 인식률 개선을 위한 선형이동 능동카메라 시스템기반 얼굴포즈 보정 기술)

  • 장승호;김영욱;박창우;박장한;남궁재찬;백준기
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.6
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    • pp.155-164
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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 and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition 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 the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.

A Study On Three-dimensional Optimized Face Recognition Model : Comparative Studies and Analysis of Model Architectures (3차원 얼굴인식 모델에 관한 연구: 모델 구조 비교연구 및 해석)

  • Park, Chan-Jun;Oh, Sung-Kwun;Kim, Jin-Yul
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.6
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    • pp.900-911
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    • 2015
  • In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

A Study on Face Recognition and Reliability Improvement Using Classification Analysis Technique

  • Kim, Seung-Jae
    • International journal of advanced smart convergence
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    • v.9 no.4
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    • pp.192-197
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    • 2020
  • In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.

Efficient 3D Model based Face Representation and Recognition Algorithmusing Pixel-to-Vertex Map (PVM)

  • Jeong, Kang-Hun;Moon, Hyeon-Joon
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.5 no.1
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    • pp.228-246
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    • 2011
  • A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a novel 3D face representation algorithm based on a pixel to vertex map (PVM) to optimize the number of vertices. We explore shape and texture coefficient vectors of the 3D model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that the proposed face representation and recognition algorithm is efficient in computation time while maintaining reasonable accuracy.