Thematische Bibliographien / Face recognition / Zeitschriftenartikel
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Zeitschriftenartikel zum Thema „Face recognition“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 4. Mai 2024

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1

Deshmukh, Sagar, Sanjay Rawat und Shubhangi Patil. „Face Recognition Technology“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30.06.2018): 1612–13. http://dx.doi.org/10.31142/ijtsrd14331.

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2

Yadav, Rakeshkumar H., Brajgopal Agarwal und Sheeba James. „Face Recognition System“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30.06.2018): 1815–18. http://dx.doi.org/10.31142/ijtsrd14453.

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3

Ounachad, Khalid, Mohamed Oualla, Abdelalim Sadiq und Abdelghani Sohar. „Face Sketch Recognition: Gender Classification and Recognition“. International Journal of Psychosocial Rehabilitation 24, Nr. 03 (18.02.2020): 1073–85. http://dx.doi.org/10.37200/ijpr/v24i3/pr200860.

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4

V, Prathama, und Thippeswamy G. „Age Invariant Face Recognition“. International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (30.06.2019): 971–76. http://dx.doi.org/10.31142/ijtsrd23572.

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5

Patel, Ibrahim, Raghavendra Kulkarni und Dr P. Nageswar Rao. „Robust Singular Value Decomposition Algorithm for Unique Faces“. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, Nr. 2 (21.06.2018): 596–603. http://dx.doi.org/10.24297/ijct.v4i2c1.4178.

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It has been read and also seen by physical encounters that there found to be seven near resembling humans by appearance .Many a times one becomes confused with respect to identification of such near resembling faces when one encounters them. The recognition of familiar faces plays a fundamental role in our social interactions. Humans are able to identify reliably a large number of faces and psychologists are interested in understanding the perceptual and cognitive mechanisms at the base of the face recognition process. As it is needed that an automated face recognition system should be faces specific, it should effectively use features that discriminate a face from others by preferably amplifying distinctive characteristics of face. Face recognition has drawn wide attention from researchers in areas of machine learning, computer vision, pattern recognition, neural networks, access control, information security, law enforcement and surveillance, smart cards etc. The paper shows that the most resembling faces can be recognized by having a unique value per face under different variations. Certain image transformations, such as intensity negation, strange viewpoint changes, and changes in lighting direction can severely disrupt human face recognition. It has been said again and again by research scholars that SVD algorithm is not good enough to classify faces under large variations but this paper proves that the SVD algorithm is most robust algorithm and can be proved effective in identifying faces under large variations as applicable to unique faces. This paper works on these aspects and tries to recognize the unique faces by applying optimized SVD algorithm.
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Reddy, Mr B. Ravinder, V. Akhil und G. Sai Preetham P. Sai Poojitha. „Profile Identification through Face Recognition“. International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (30.04.2019): 1482–83. http://dx.doi.org/10.31142/ijtsrd23439.

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7

Garg, Deepika. „Face Recognition“. IOSR Journal of Engineering 02, Nr. 07 (Juli 2012): 128–33. http://dx.doi.org/10.9790/3021-0271128133.

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Zhao, W., R. Chellappa, P. J. Phillips und A. Rosenfeld. „Face recognition“. ACM Computing Surveys 35, Nr. 4 (Dezember 2003): 399–458. http://dx.doi.org/10.1145/954339.954342.

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9

Gross, Charles G., und Justine Sergent. „Face recognition“. Current Biology 2, Nr. 5 (Mai 1992): 235. http://dx.doi.org/10.1016/0960-9822(92)90354-d.

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10

.Gross, Charles G., und Justine Sergent. „Face recognition“. Current Opinion in Neurobiology 2, Nr. 2 (April 1992): 156–61. http://dx.doi.org/10.1016/0959-4388(92)90004-5.

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Xiong, Yijie. „Face recognition based on machine learning“. Applied and Computational Engineering 6, Nr. 1 (14.06.2023): 1100–1105. http://dx.doi.org/10.54254/2755-2721/6/20230407.

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Due to its widespread use, face recognition has emerged in the past 20 years as one of the most pervasive biometric identification technology disciplines. This paper briefly summarizes the history of face recognitions development, identifies the technologys present use cases, introduces the main methods of face recognition in detail from the perspective of machine learning and prospects for the future development of this technology. The result shows that this technology still faces many challenges, such as the problem of recognizing different expressions on the same face, the problem of recognizing twins and similar faces, the problem of using the color information of color face images efficiently and so on.
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Prof Sami M. Halwani, Prof M. V. Ramana Murthy und Prof S. B. Thorat. „Laplacian Faces: A Face Recognition Tool“. International Journal of Networked Computing and Advanced Information Management 2, Nr. 1 (30.04.2012): 1–7. http://dx.doi.org/10.4156/ijncm.vol2.issue1.1.

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Schwartz, Linoy, und Galit Yovel. „Are Faces Important for Face Recognition?“ Journal of Vision 15, Nr. 12 (01.09.2015): 703. http://dx.doi.org/10.1167/15.12.703.

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Tovée, Martin J. „Face Recognition: What are faces for?“ Current Biology 5, Nr. 5 (Mai 1995): 480–82. http://dx.doi.org/10.1016/s0960-9822(95)00096-0.

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He, Yunhui, Li Zhao und Cairong Zou. „Face recognition using common faces method“. Pattern Recognition 39, Nr. 11 (November 2006): 2218–22. http://dx.doi.org/10.1016/j.patcog.2006.04.037.

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米, 勇. „Face Recognition Based on Feature Faces“. Computer Science and Application 09, Nr. 01 (2019): 127–31. http://dx.doi.org/10.12677/csa.2019.91015.

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Afrin, Sadia, Maria Tasnim und Md Rafiqul Islam. „Human Face Recognition Using Eigen Vector-Based Recognition System“. International Journal of Research and Scientific Innovation X, Nr. VI (2023): 127–34. http://dx.doi.org/10.51244/ijrsi.2023.10617.

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Face recognition is an algorithm that can recognize or verify a query face among a large number of faces in the enrollment database. Face recognition is a crucial and difficult area of computer vision. This study demonstrates a system that can recognize a human face by comparing the facial structure to that of another individual or a well-known individual, which is accomplished by the use of frontal several summarizations. Many researchers have done their work on face recognition and also applied it by using different methods. We made use of an eigenvector-based recognition system as a method for recognizing faces. The face recognition system is highly accurate and is one of the most powerful surveillance tools ever made. But this face recognition technology is quite costly for developing countries like Bangladesh. In this study, we have used a face recognition system for our security purpose using an eigenvector-based face recognition system with the help of MATLAB software and a Raspberry Pi camera for security purposes which minimizes the cost, and this process we have used is quite affordable
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Abbas, Hawraa H., Bilal Z. Ahmed und Ahmed Kamil Abbas. „3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms“. Cybernetics and Information Technologies 19, Nr. 2 (01.06.2019): 28–37. http://dx.doi.org/10.2478/cait-2019-0013.

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Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.
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Priya, B. Lakshmi, und Dr M. Pushpa Rani Rani. „Face Recognition System Techniques and Approaches“. Indian Journal of Applied Research 4, Nr. 4 (01.10.2011): 109–13. http://dx.doi.org/10.15373/2249555x/apr2014/32.

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R.S., Dr Sabeenian. „Attendance Authentication System Using Face Recognition“. Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (31.03.2020): 1235–48. http://dx.doi.org/10.5373/jardcs/v12sp4/20201599.

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Mishra, K. Ravikanth, D. Brahmeswara Rao und A. Dinesh Chowdary. „Student Library Attendance using Face Recognition“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30.04.2018): 1238–40. http://dx.doi.org/10.31142/ijtsrd11281.

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22

Malkapurkar, Anagha V., und Prof Sachin Murarka. „Using LBP histogram for Face Recognition“. International Journal of Scientific Research 1, Nr. 7 (01.06.2012): 176–77. http://dx.doi.org/10.15373/22778179/dec2012/64.

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23

Kanth, Pooja L., und Salva Biswal. „Attendance Marking System Using Face Recognition“. Indian Journal of Science and Technology 12, Nr. 48 (20.12.2019): 1–3. http://dx.doi.org/10.17485/ijst/2019/v12i48/145821.

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Rhodes, Gillian. „Adaptive Coding and Face Recognition“. Current Directions in Psychological Science 26, Nr. 3 (Juni 2017): 218–24. http://dx.doi.org/10.1177/0963721417692786.

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Face adaptation generates striking face aftereffects, but is this adaptation useful? The answer appears to be yes, with several lines of evidence suggesting that it contributes to our face-recognition ability. Adaptation to face identity is reduced in a variety of clinical populations with impaired face recognition. In addition, individual differences in face adaptation are linked to face-recognition ability in typical adults. People who adapt more readily to new faces are better at recognizing faces. This link between adaptation and recognition holds for both identity and expression recognition. Adaptation updates face norms, which represent the typical or average properties of the faces we experience. By using these norms to code how faces differ from average, the visual system can make explicit the distinctive information that we need to recognize faces. Thus, adaptive norm-based coding may help us to discriminate and recognize faces despite their similarity as visual patterns.
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Yao, Min, und Hiroshi Nagahashi. „ILLUMINATION INSENSITIVE FACE REPRESENTATION FOR FACE RECOGNITION BASED ON MODIFIED WEBERFACE“. International Journal of Advances in Engineering & Technology 6, Nr. 5 (01.11.2013): 1995–2005. http://dx.doi.org/10.7323/ijaet/v6_iss5_06.

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Phillips, P. Jonathon, Amy N. Yates, Ying Hu, Carina A. Hahn, Eilidh Noyes, Kelsey Jackson, Jacqueline G. Cavazos et al. „Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms“. Proceedings of the National Academy of Sciences 115, Nr. 24 (29.05.2018): 6171–76. http://dx.doi.org/10.1073/pnas.1721355115.

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Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.
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Romanyuk, Olexandr N., Sergey I. Vyatkin, Sergii V. Pavlov, Pavlo I. Mykhaylov, Roman Y. Chekhmestruk und Ivan V. Perun. „FACE RECOGNITION TECHNIQUES“. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, Nr. 1 (30.03.2020): 52–57. http://dx.doi.org/10.35784/iapgos.922.

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The problem of face recognition is discussed. The main methods of recognition are considered. The calibrated stereo pair for the face and calculating the depth map by the correlation algorithm are used. As a result, a 3D mask of the face is obtained. Using three anthropomorphic points, then constructed a coordinate system that ensures a possibility of superposition of the tested mask.
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Said, Ebrahem, und Mona Nasr. „Face Recognition System“. International Journal of Advanced Networking and Applications 12, Nr. 02 (2020): 4567–74. http://dx.doi.org/10.35444/ijana.2020.12205.

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Rajput, Ankit. „Face Recognition Technology“. International Journal for Research in Applied Science and Engineering Technology 7, Nr. 3 (31.03.2019): 859–62. http://dx.doi.org/10.22214/ijraset.2019.3150.

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Sabharwal, Himani, und Akash Tayal. „Human Face Recognition“. International Journal of Computer Applications 104, Nr. 11 (18.10.2014): 1–3. http://dx.doi.org/10.5120/18243-9173.

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31

Liu, Tongyang, Xiaoyu Xiang, Qian Lin und Jan P. Allebach. „Face Set Recognition“. Electronic Imaging 2019, Nr. 8 (13.01.2019): 400–1. http://dx.doi.org/10.2352/issn.2470-1173.2019.8.imawm-400.

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Saxenna, Yasharth. „Face Recognition System“. International Journal for Research in Applied Science and Engineering Technology 8, Nr. 7 (31.07.2020): 1883–85. http://dx.doi.org/10.22214/ijraset.2020.30704.

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33

Tang, X., und X. Wang. „Face Sketch Recognition“. IEEE Transactions on Circuits and Systems for Video Technology 14, Nr. 1 (Januar 2004): 50–57. http://dx.doi.org/10.1109/tcsvt.2003.818353.

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34

S.G, Rajeshwari. „Human Face Recognition“. International Journal for Research in Applied Science and Engineering Technology 8, Nr. 6 (30.06.2020): 638–43. http://dx.doi.org/10.22214/ijraset.2020.6104.

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35

Dzhangarov, A. I., M. A. Suleymanova und A. L. Zolkin. „Face recognition methods“. IOP Conference Series: Materials Science and Engineering 862 (28.05.2020): 042046. http://dx.doi.org/10.1088/1757-899x/862/4/042046.

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36

Liu, Yun-Fu, Jing-Ming Guo, Po-Hsien Liu, Jiann-Der Lee und Chen-Chieh Yao. „Panoramic Face Recognition“. IEEE Transactions on Circuits and Systems for Video Technology 28, Nr. 8 (August 2018): 1864–74. http://dx.doi.org/10.1109/tcsvt.2017.2693682.

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37

Moghaddam, Baback, Tony Jebara und Alex Pentland. „Bayesian face recognition“. Pattern Recognition 33, Nr. 11 (November 2000): 1771–82. http://dx.doi.org/10.1016/s0031-3203(99)00179-x.

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38

Russell, R., B. Duchaine und K. Nakayama. „Extraordinary face recognition“. Journal of Vision 7, Nr. 9 (23.03.2010): 629. http://dx.doi.org/10.1167/7.9.629.

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39

Voth, D. „Face recognition technology“. IEEE Intelligent Systems 18, Nr. 3 (Mai 2003): 4–7. http://dx.doi.org/10.1109/mis.2003.1200719.

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40

Bruce, Vicki, und Andy Young. „Understanding face recognition“. British Journal of Psychology 77, Nr. 3 (August 1986): 305–27. http://dx.doi.org/10.1111/j.2044-8295.1986.tb02199.x.

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41

Kroeker, Kirk L. „Face recognition breakthrough“. Communications of the ACM 52, Nr. 8 (August 2009): 18–19. http://dx.doi.org/10.1145/1536616.1536623.

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42

SWARUPA, N. V. S. L., und D. SUPRIYA. „Face Recognition System“. International Journal of Computer Applications 1, Nr. 29 (25.02.2010): 36–42. http://dx.doi.org/10.5120/577-314.

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43

Kemal Ekenel, Hazim, und Bülent Sankur. „Multiresolution face recognition“. Image and Vision Computing 23, Nr. 5 (Mai 2005): 469–77. http://dx.doi.org/10.1016/j.imavis.2004.09.002.

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44

Nagesh, Mr P. „Face Recognition Systems“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 3 (31.03.2023): 962–64. http://dx.doi.org/10.22214/ijraset.2023.49567.

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Abstract: Face recognition systems have become increasingly popular and important in recent years due to their various applications in security, surveillance, and human-computer interaction. These systems use algorithms to detect and recognize human faces in images or videos, and can be trained to identify individuals with high accuracy.
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45

Alamand, M. S., und A. F. Al-Samman. „Invariant face recognition“. Microwave and Optical Technology Letters 30, Nr. 6 (2001): 418–23. http://dx.doi.org/10.1002/mop.1333.

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Bhange, Prof Anup. „Face Detection System with Face Recognition“. International Journal for Research in Applied Science and Engineering Technology 10, Nr. 1 (31.01.2022): 1095–100. http://dx.doi.org/10.22214/ijraset.2022.39976.

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Abstract: The face is one of the easiest way to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Now a days Human Face Detection and Recognition become a major field of interest in current research because there is no deterministic algorithm to find faces in a given image. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is recognition, which recognize (by comparing face with picture or either with image captured through webcam) a face as an individual. In face detection and recognition technology, it is mainly introduced from the OpenCV method. Face recognition is one of the much-studied biometrics technology and developed by experts. The area of this project face detection system with face recognition is Image processing. The software requirement for this project is Python. Keywords: face detection, face recognition, cascade_classifier, LBPH.
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47

Bo-Gun Park, Kyoung-Mu Lee und Sang-Uk Lee. „Face recognition using face-ARG matching“. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, Nr. 12 (Dezember 2005): 1982–88. http://dx.doi.org/10.1109/tpami.2005.243.

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48

Kafai, Mehran, Le An und Bir Bhanu. „Reference Face Graph for Face Recognition“. IEEE Transactions on Information Forensics and Security 9, Nr. 12 (Dezember 2014): 2132–43. http://dx.doi.org/10.1109/tifs.2014.2359548.

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49

Wu, Lifang, und Lansun Shen. „Face recognition from front-view face“. Journal of Electronics (China) 20, Nr. 1 (Januar 2003): 45–50. http://dx.doi.org/10.1007/s11767-003-0086-7.

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50

Salama, Ramiz, und Mohamed Nour. „Security Technologies Using Facial Recognition“. Global Journal of Computer Sciences: Theory and Research 13, Nr. 1 (31.03.2023): 01–27. http://dx.doi.org/10.18844/gjcs.v13i1.8294.

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Abstract Faces are one of the simplest methods to determine a person's identity. Face recognition is a unique identifying method that uses an individual's traits to determine the identity of that individual. The proposed recognition process is divided into two stages: face recognition and object recognition. Unless the item is very close, this procedure is very rapid for humans. The recognition of human faces is introduced next. The stage is then reproduced and used as a model for facial image recognition (face recognition). That's one of the professionally created and well-researched biometrics procedures. The eigenface approach and the Fisher face method are two common face recognition pattern algorithms that have been developed. Recognition of facial images The Eigenface approach is based on the reduction of face dimensional space for facial traits using Principal Component Analysis (PCA). The major goal of applying PCA on face recognition was to generate Eigen faces (face space) by identifying the eigenvector corresponding to the face image's biggest eigenvalue. Image processing and security systems are areas of interest in this research face recognition integrated into a security system. Keywords: face recognition, security systems, camera, python;
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