• Title/Summary/Keyword: Face Recognition

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Performance Analysis of Face Recognition by Distance according to Image Normalization and Face Recognition Algorithm (영상 정규화 및 얼굴인식 알고리즘에 따른 거리별 얼굴인식 성능 분석)

  • Moon, Hae-Min;Pan, Sung Bum
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.23 no.4
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    • pp.737-742
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    • 2013
  • The surveillance system has been developed to be intelligent which can judge and cope by itself using human recognition technique. The existing face recognition is excellent at a short distance but recognition rate is reduced at a long distance. In this paper, we analyze the performance of face recognition according to interpolation and face recognition algorithm in face recognition using the multiple distance face images to training. we use the nearest neighbor, bilinear, bicubic, Lanczos3 interpolations to interpolate face image and PCA and LDA to face recognition. The experimental results show that LDA-based face recognition with bilinear interpolation provides performance in face recognition.

Pose-normalized 3D Face Modeling for Face Recognition

  • Yu, Sun-Jin;Lee, Sang-Youn
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.12C
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    • pp.984-994
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    • 2010
  • Pose variation is a critical problem in face recognition. Three-dimensional(3D) face recognition techniques have been proposed, as 3D data contains depth information that may allow problems of pose variation to be handled more effectively than with 2D face recognition methods. This paper proposes a pose-normalized 3D face modeling method that translates and rotates any pose angle to a frontal pose using a plane fitting method by Singular Value Decomposition(SVD). First, we reconstruct 3D face data with stereo vision method. Second, nose peak point is estimated by depth information and then the angle of pose is estimated by a facial plane fitting algorithm using four facial features. Next, using the estimated pose angle, the 3D face is translated and rotated to a frontal pose. To demonstrate the effectiveness of the proposed method, we designed 2D and 3D face recognition experiments. The experimental results show that the performance of the normalized 3D face recognition method is superior to that of an un-normalized 3D face recognition method for overcoming the problems of pose variation.

Modern Face Recognition using New Masked Face Dataset Generated by Deep Learning (딥러닝 기반의 새로운 마스크 얼굴 데이터 세트를 사용한 최신 얼굴 인식)

  • Pann, Vandet;Lee, Hyo Jong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2021.11a
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    • pp.647-650
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    • 2021
  • The most powerful and modern face recognition techniques are using deep learning methods that have provided impressive performance. The outbreak of COVID-19 pneumonia has spread worldwide, and people have begun to wear a face mask to prevent the spread of the virus, which has led existing face recognition methods to fail to identify people. Mainly, it pushes masked face recognition has become one of the most challenging problems in the face recognition domain. However, deep learning methods require numerous data samples, and it is challenging to find benchmarks of masked face datasets available to the public. In this work, we develop a new simulated masked face dataset that we can use for masked face recognition tasks. To evaluate the usability of the proposed dataset, we also retrained the dataset with ArcFace based system, which is one the most popular state-of-the-art face recognition methods.

Comparison of Computer and Human Face Recognition According to Facial Components

  • Nam, Hyun-Ha;Kang, Byung-Jun;Park, Kang-Ryoung
    • Journal of Korea Multimedia Society
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    • v.15 no.1
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    • pp.40-50
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    • 2012
  • Face recognition is a biometric technology used to identify individuals based on facial feature information. Previous studies of face recognition used features including the eye, mouth and nose; however, there have been few studies on the effects of using other facial components, such as the eyebrows and chin, on recognition performance. We measured the recognition accuracy affected by these facial components, and compared the differences between computer-based and human-based facial recognition methods. This research is novel in the following four ways compared to previous works. First, we measured the effect of components such as the eyebrows and chin. And the accuracy of computer-based face recognition was compared to human-based face recognition according to facial components. Second, for computer-based recognition, facial components were automatically detected using the Adaboost algorithm and active appearance model (AAM), and user authentication was achieved with the face recognition algorithm based on principal component analysis (PCA). Third, we experimentally proved that the number of facial features (when including eyebrows, eye, nose, mouth, and chin) had a greater impact on the accuracy of human-based face recognition, but consistent inclusion of some feature such as chin area had more influence on the accuracy of computer-based face recognition because a computer uses the pixel values of facial images in classifying faces. Fourth, we experimentally proved that the eyebrow feature enhanced the accuracy of computer-based face recognition. However, the problem of occlusion by hair should be solved in order to use the eyebrow feature for face recognition.

Face Recognition Based on PCA on Wavelet Subband of Average-Half-Face

  • Satone, M.P.;Kharate, G.K.
    • Journal of Information Processing Systems
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    • v.8 no.3
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    • pp.483-494
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    • 2012
  • Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly.

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.

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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Face Recognition Using Convolutional Neural Network and Stereo Images (Convolutional Neural Network와 Stereo Image를 이용한 얼굴 인식)

  • Ki, Cheol-min;Cho, Tai-Hoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2016.05a
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    • pp.359-362
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    • 2016
  • Face is an information unique to each person such as Iris, fingerprints, etc,. Research on face recognition are in progress continuously from the past to the present. Through these research, various face recognition methods have appeared. Among these methods, there are face recognition algorithms using the face data composed in stereo. In this paper, Convolutional Neural Network with Stereo Images as input was used for face recognition. This method showed better performance than the result of stereo face recognition using PCA that is used frequently in face recognition.

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Untact Face Recognition System Based on Super-resolution in Low-Resolution Images (초고해상도 기반 비대면 저해상도 영상의 얼굴 인식 시스템)

  • Bae, Hyeon Bin;Kwon, Oh Seol
    • Journal of Korea Multimedia Society
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    • v.23 no.3
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    • pp.412-420
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    • 2020
  • This paper proposes a performance-improving face recognition system based on a super resolution method for low-resolution images. The conventional face recognition algorithm has a rapidly decreased accuracy rate due to small image resolution by a distance. To solve the previously mentioned problem, this paper generates a super resolution images based o deep learning method. The proposed method improved feature information from low-resolution images using a super resolution method and also applied face recognition using a feature extraction and an classifier. In experiments, the proposed method improves the face recognition rate when compared to conventional methods.

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.