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===위키데이터===
 
===위키데이터===
 
* ID :  [https://www.wikidata.org/wiki/Q1192553 Q1192553]
 
* ID :  [https://www.wikidata.org/wiki/Q1192553 Q1192553]
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===Spacy 패턴 목록===
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* [{'LOWER': 'facial'}, {'LOWER': 'recognition'}, {'LEMMA': 'system'}]
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* [{'LOWER': 'face'}, {'LEMMA': 'recognition'}]
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* [{'LOWER': 'facial'}, {'LEMMA': 'recognition'}]

2021년 2월 17일 (수) 00:46 기준 최신판

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말뭉치

  1. Telpo Android OS face recognition machines have good compatibility and extensibility.[1]
  2. Further, because it is the first step in a broader face recognition system, face detection must be robust.[2]
  3. A face recognition system is expected to identify faces present in images and videos automatically.[2]
  4. The holistic approaches dominated the face recognition community in the 1990s.[2]
  5. There are perhaps four milestone systems on deep learning for face recognition that drove these innovations; they are: DeepFace, the DeepID series of systems, VGGFace, and FaceNet.[2]
  6. Considering roughly presented elements above of the complex process of face recognition, a number of limitations and imperfections can be seen.[3]
  7. It is the fact that face recognition systems are still not very robust regarding to deviations from ideal face image.[3]
  8. Recent advances in automated face analysis, pattern recognition and machine learning have made it possible to develop automatic face recognition systems to address these applications.[3]
  9. Being part of a biometric technology, automated face recognition has a plenty of desirable properties.[3]
  10. Research on face recognition to reliably locate a face in an image that contains other objects gained traction in the early 1990s with the principle component analysis (PCA).[4]
  11. LDA Fisherfaces became dominantly used in PCA feature based face recognition.[4]
  12. Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject's face.[4]
  13. Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition.[4]
  14. Face recognition is a technology capable of identifying or verifying a subject through an image, video or any audiovisual element of his face.[5]
  15. The objective of face recognition is, from the incoming image, to find a series of data of the same face in a set of training images in a database.[5]
  16. Thanks to the use of artificial intelligence (AI) and machine learning technologies, face recognition systems can operate with the highest safety and reliability standards.[5]
  17. Face recognition uses focus on verification or authentication.[5]
  18. With the constant support of our dexterous crew of technocrats, we are fulfilling the varied requirements of clients by manufacturing and supplying optimum quality Face Recognition Machine.[6]
  19. We are actively engaged in offering high performance Face Recognition Machine, which is procured from certified vendors of the industry.[6]
  20. The offered face recognition machine is robustly designed by our reliable vendors in compliance with international quality standards.[6]
  21. This face recognition machine is widely used in various corporate sectors, offices, etc.[6]
  22. This article opens up what face recognition is from a technology perspective, and how deep learning increases its capacities.[7]
  23. Realizing the weaknesses of face recognition systems, data scientists went further.[7]
  24. By applying traditional computer vision techniques and deep learning algorithms, they fine-tuned the face recognition system to prevent attacks and enhance accuracy.[7]
  25. Deep learning is one of the most novel ways to improve face recognition technology.[7]
  26. Even though face recognition is promising, it does have some flaws.[8]
  27. Simple face recognition systems could easily be spoofed by using paper-based images from the internet.[8]
  28. Face recognition is only the beginning of implementing this method.[9]
  29. A classical 2D face recognition system operates on images or videos obtained from surveillance systems, commercial/private cameras, CCTV, or similar everyday hardware.[10]
  30. To sum up, all holistic methods are prevalent in the implementation of face recognition systems.[10]
  31. It is generally known that in this perspective, the variations in lighting that contemplate face recognition present one of the significant challenges.[10]
  32. Attention and fixations play a crucial function in human face recognition.[10]
  33. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications.[11]
  34. Much like databases today, face recognition will be used for all sorts of things in many parts of societies, including many things that don’t today look like a face recognition use case.[12]
  35. We might be comfortable with our bank using face recognition as well.[12]
  36. Part of the experience of databases, though, was that some things create discomfort only because they’re new and unfamiliar, and face recognition is the same.[12]
  37. The cutting edge work is still limited to a relatively small number of companies and institutions, but ‘face recognition’ is now freely available to any software company to build with.[12]
  38. Face recognition is a method for identifying an unknown person or authenticating the identity of a specific person from their face.[13]
  39. Another approach to face recognition is to normalize and compress 2-D facial images, and to compare these with a database of similarly normalized and compressed images.[13]
  40. Three-dimensional face recognition uses 3-D sensors to capture the facial image, or reconstructs the 3-D image from three 2-D tracking cameras pointed at different angles.[13]
  41. Adding skin texture analysis to 2-D or 3-D face recognition can improve the recognition accuracy by 20 to 25 percent, especially in the cases of look-alikes and twins.[13]
  42. Built using dlib's state-of-the-art face recognition built with deep learning.[14]
  43. Face recognition can be done in parallel if you have a computer with multiple CPU cores.[14]
  44. The face recognition model is trained on adults and does not work very well on children.[14]
  45. MX RT106F crossover MCU, enabling developers to quickly and easily add face recognition capabilities to their products.[15]
  46. “Face recognition is a very deceiving term, technically, because there’s no limit,” he concludes.[16]
  47. It includes high-quality cameras and API that easily integrates face recognition analytics with existing technology systems.[17]
  48. We present data comparing state-of-the-art face recognition technology with the best human face identifiers.[18]
  49. First, untrained “superrecognizers” from the general public perform surprisingly well on laboratory-based face recognition studies (1).[18]
  50. Second, wisdom-of-crowds effects for face recognition, implemented by averaging individuals’ judgments, can boost performance substantially over the performance of a person working alone (2⇓⇓–5).[18]
  51. Multiple laboratory-based face recognition tests of these individuals indicate that highly accurate face identification can be achieved by people with no professional training (1).[18]
  52. How Facial Recognition Algorithm Works Which algorithms are used in face recognition?[19]
  53. There are more subtle ways in which face recognition algorithms are changing our everyday life in meaningful ways too, proving that this technology is still far from infallible.[19]
  54. According to one aspect of the present technique, a system and method of face recognition is provided.[20]
  55. A face recognition module identifies at least one likely candidate from a plurality of stored images based on the transformed model face.[20]
  56. 4 is a flow chart illustrating a face authentication process of the exemplary face recognition system illustrated in FIG.[20]
  57. Each time an image is captured, the face recognition system 10 may utilize the captured image during the face recognition process.[20]
  58. Humans show race bias in face recognition and this is a finding that has been replicated hundreds of times at this point.[21]
  59. Face recognition is a method of identifying or verifying the identity of an individual using their face.[22]
  60. Face recognition systems can be used to identify people in photos, video, or in real-time.[22]
  61. But face recognition data can be prone to error, which can implicate people for crimes they haven’t committed.[22]
  62. Additionally, face recognition has been used to target people engaging in protected speech.[22]
  63. This is where you can store your processed face recognition videos.[23]
  64. : This is where you can store your processed face recognition videos.[23]

소스

  1. ↑ Face Recognition Machine Manufacturer
  2. ↑ 2.0 2.1 2.2 2.3 A Gentle Introduction to Deep Learning for Face Recognition
  3. ↑ 3.0 3.1 3.2 3.3 Face Recognition: Issues, Methods and Alternative Applications
  4. ↑ 4.0 4.1 4.2 4.3 Facial recognition system
  5. ↑ 5.0 5.1 5.2 5.3 Face Recognition: how it works and its safety
  6. ↑ 6.0 6.1 6.2 6.3 Face Recognition Machine
  7. ↑ 7.0 7.1 7.2 7.3 Face Recognition App Development Using Deep Learning
  8. ↑ 8.0 8.1 How machine learning changed facial recognition technology?
  9. ↑ How to build a face detection and recognition system
  10. ↑ 10.0 10.1 10.2 10.3 Past, Present, and Future of Face Recognition: A Review
  11. ↑ Face recognition based on extreme learning machine
  12. ↑ 12.0 12.1 12.2 12.3 Face recognition and AI ethics — Benedict Evans
  13. ↑ 13.0 13.1 13.2 13.3 What is face recognition? AI for Big Brother
  14. ↑ 14.0 14.1 14.2 ageitgey/face_recognition: The world's simplest facial recognition api for Python and the command line
  15. ↑ NXP EdgeReady MCU-Based Solution for Face Recognition
  16. ↑ Who’s using your face? The ugly truth about facial recognition
  17. ↑ 9 Best Facial Recognition Software For Your PC
  18. ↑ 18.0 18.1 18.2 18.3 Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
  19. ↑ 19.0 19.1 Facial Recognition Algorithms for Machine Learning: Application and Safety
  20. ↑ 20.0 20.1 20.2 20.3 US20060120571A1 - System and method for passive face recognition - Google Patents
  21. ↑ The Accuracy of Machines in Facial Recognition
  22. ↑ 22.0 22.1 22.2 22.3 Face Recognition
  23. ↑ 23.0 23.1 Face recognition with OpenCV, Python, and deep learning

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Spacy 패턴 목록

  • [{'LOWER': 'facial'}, {'LOWER': 'recognition'}, {'LEMMA': 'system'}]
  • [{'LOWER': 'face'}, {'LEMMA': 'recognition'}]
  • [{'LOWER': 'facial'}, {'LEMMA': 'recognition'}]
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