• Title, Summary, Keyword: Computer-based face recognition

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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.

Modified distance measures for PCA-based face recognition

  • Song Young-Jun;Kim Young-Gil;Kim Nam
    • International Journal of Contents
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    • v.1 no.2
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    • pp.1-4
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    • 2005
  • In this paper, we compare 5 weighted distance measures between feature vectors with respect to the recognition performance of the principal component analysis(PCA)-based face recognition method, and propose modified weighted distance. The proposed method was modification of z, the weighted vector. The simulation was performed using the ORL face database, showed the best result for some weighted distances such as weighted manhattan, weighted angle-based, weighted modified manhattan, and weighted modified SSE. We also showed that using some various values of z(weighted values) we can achieve better recognition results that using the existing weighted value.

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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.

A Novel Approach to Mugshot Based Arbitrary View Face Recognition

  • Zeng, Dan;Long, Shuqin;Li, Jing;Zhao, Qijun
    • Journal of the Optical Society of Korea
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    • v.20 no.2
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    • pp.239-244
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    • 2016
  • Mugshot face images, routinely collected by police, usually contain both frontal and profile views. Existing automated face recognition methods exploited mugshot databases by enlarging the gallery with synthetic multi-view face images generated from the mugshot face images. This paper, instead, proposes to match the query arbitrary view face image directly to the enrolled frontal and profile face images. During matching, the 3D face shape model reconstructed from the mugshot face images is used to establish corresponding semantic parts between query and gallery face images, based on which comparison is done. The final recognition result is obtained by fusing the matching results with frontal and profile face images. Compared with previous methods, the proposed method better utilizes mugshot databases without using synthetic face images that may have artifacts. Its effectiveness has been demonstrated on the Color FERET and CMU PIE databases.

Classroom Roll-Call System Based on ResNet Networks

  • Zhu, Jinlong;Yu, Fanhua;Liu, Guangjie;Sun, Mingyu;Zhao, Dong;Geng, Qingtian;Su, Jinbo
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1145-1157
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    • 2020
  • A convolution neural networks (CNNs) has demonstrated outstanding performance compared to other algorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers have proposed a residual network to ease the training for recognition accuracy improvement. In this study, a novel face recognition model based on game theory for call-over in the classroom was proposed. In the proposed scheme, an image with multiple faces was used as input, and the residual network identified each face with a confidence score to form a list of student identities. Face tracking of the same identity or low confidence were determined to be the optimisation objective, with the game participants set formed from the student identity list. Game theory optimises the authentication strategy according to the confidence value and identity set to improve recognition accuracy. We observed that there exists an optimal mapping relation between face and identity to avoid multiple faces associated with one identity in the proposed scheme and that the proposed game-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.

Viewpoint Unconstrained Face Recognition Based on Affine Local Descriptors and Probabilistic Similarity

  • Gao, Yongbin;Lee, Hyo Jong
    • Journal of Information Processing Systems
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    • v.11 no.4
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    • pp.643-654
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    • 2015
  • 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 propose using the combination of Affine Scale Invariant Feature Transform (SIFT) and Probabilistic Similarity for face recognition under a large viewpoint change. Affine SIFT is an extension of SIFT algorithm to detect affine invariant local descriptors. Affine SIFT generates a series of different viewpoints using affine transformation. In this way, it allows for a viewpoint difference between the gallery face and probe face. However, the human face is not planar as it contains significant 3D depth. Affine SIFT does not work well for significant change in pose. To complement this, we combined it with probabilistic similarity, which gets the log likelihood between the probe and gallery face based on sum of squared difference (SSD) distribution in an offline learning process. Our experiment results show that our framework achieves impressive better recognition accuracy than other algorithms compared on the FERET database.

Face Tracking System for Efficient Face Recognition in Intelligent Digital TV (지능형 디지털 TV에서 효율적인 얼굴 인식을 위한 얼굴 추적 시스템 구현)

  • Kwon, Ki-Poong;Kim, Seung-Gu;Kim, Seung-Kyun;Hwang, Min-Cheol;Ko, Sung-Jea
    • Proceedings of the IEEK Conference
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    • pp.267-268
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    • 2006
  • Advanced TV makes the life more convenient for the viewers and it is based on the recognition technology. In this paper, we propose the implementation of face tracking system for efficient face recognition in intelligent digital TV. To recognize the face, face detection should be performed earlier. We use the motion information to track the face. Continuous face tracking is possible by using continuous detected face region and motion information. Thus the computational complexity of the recognition module in the whole system can be reduced.

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The Facial Expression Recognition using the Inclined Face Geometrical information

  • Zhao, Dadong;Deng, Lunman;Song, Jeong-Young
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.881-886
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    • 2012
  • The paper is facial expression recognition based on the inclined face geometrical information. In facial expression recognition, mouth has a key role in expressing emotions, in this paper the features is mainly based on the shapes of mouth, followed by eyes and eyebrows. This paper makes its efforts to disperse every feature values via the weighting function and proposes method of expression classification with excellent classification effects; the final recognition model has been constructed.

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A Survey of Face Recognition Techniques

  • Jafri, Rabia;Arabnia, Hamid R.
    • Journal of Information Processing Systems
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    • v.5 no.2
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    • pp.41-68
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    • 2009
  • Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

Using Spatial Pyramid Based Local Descriptor for Face Recognition (공간 계층적 구조 기반 지역 기술자 활용 얼굴인식 기술)

  • Kim, Kyeong Tae;Choi, Jae Young
    • Journal of Korea Multimedia Society
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    • v.20 no.5
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    • pp.758-768
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    • 2017
  • In this paper, we present a novel method to extract face representation based on multi-resolution spatial pyramid. In our method, a face is subdivided into increasingly finer sub-regions (local regions) and represented at multiple levels of histogram representations. To cope with misaligned problem, patch-based local descriptor extraction has been also developed in a novel way. To preserve multiple levels of detail in local characteristics and also encode holistic spatial configuration, histograms from all levels of spatial pyramid are integrated by using dimensionality reduction and feature combination, leading to our spatial-pyramid face feature representation. We incorporate our proposed face features into general face recognition pipeline and achieve state-of-the-art results on challenging face recognition problems.