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The Robotics Institute Carnegie Mellon University : Robotics Educa…
- https://www.ri.cmu.edu/pub_files/pub4/sakai_t_1972_1/sakai_t_1972_1.pdf#:~:text=Research%20on%20human%E2%80%94face%20identification%20has%20been%20done%20by,drawn%20by%20humans%20on%20the%20proj%20ected%20photographs.
- none
Computer analysis and classification of photographs of …
- https://www.semanticscholar.org/paper/Computer-analysis-and-classification-of-photographs-Sakai/0fb70e696256866e3a28598bcd640ef1be9db500
- Computer analysis and classification of photographs of human faces @inproceedings{Sakai1973ComputerAA, title={Computer analysis and classification of photographs of human faces}, author={Toshiyuki Sakai}, year={1973} } T. Sakai; Published 1973; Computer Science
The Robotics Institute Carnegie Mellon University
- https://ri.cmu.edu/publications/computer-analysis-and-classification-of-photographs-of-human-faces/
- The Robotics Institute Carnegie Mellon University
Computer analysis of face beauty: A survey - ScienceDirect
- https://www.sciencedirect.com/science/article/pii/S1077314214000939
- A useful tool for face analysis applications is the “face space” paradigm, used in both human science, for modeling human perception, and in computer analysis. In the computer science area it was first introduced in . The general idea proposed was that, after proper normalization, the pixel array of a face image can be represented as a point, or a vector, in an …
Classification of human face images using principal …
- https://www.researchgate.net/publication/261225717_Classification_of_human_face_images_using_principal_components
- This paper describes a novel approach of using principal components analysis (PCA) for recognizing the human faces in an efficient manner. The idea builds the concept of …
A Complete Guide to Image Classification in 2022 - viso.ai
- https://viso.ai/computer-vision/image-classification/
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Classification of human face images using principal …
- https://ieeexplore.ieee.org/document/4136993/
- Discrete wavelet transform (DWT) is used to compress images for classification. Euclidean distance is used to classify the principal components of the face images. For real time system, it is suggested to use this technique as preprocessing step for near ideal results.
Computer Vision and Image Classification -A study
- https://www.dexlabanalytics.com/blog/computer-vision-and-image-classification-a-study
- Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. With computer vision, our computer can extract, analyze and understand useful information from an individual ...
The Robotics Institute Carnegie Mellon University : …
- https://www.ri.cmu.edu/pub_files/pub4/sakai_t_1972_1/sakai_t_1972_1.pdf
- machine system, in which a computer classifies human faces based on the fiducial marks drawn by humans on the proj ected photographs. Harmon and his group [2] recently constructed an interactive system for face identification that takes advantage both of the human's superiority in describing face features and of the machine's superiority in making decisions based on …
How a Computer Looks at Pictures: Image Classification
- https://medium.datadriveninvestor.com/how-a-computer-looks-at-pictures-image-classification-a4992a83f46b
- There are two common ways to do this when it comes to Image Processing: Greyscale - A range of grey shades from white to black. When using the Greyscale, the computer assigns each pixel a value (in numbers) based on its level of darkness. 2. RGB Values - A combination of red, green, and blue. Once a colour is given a RGB Value the computer ...
A Hybrid Approach to Human Face Detection
- https://www.ijcaonline.org/journal/number13/pxc387442.pdf
- classification of photographs of human faces”. Proc. First USA-Japan Computer Conference, 1972 3) T. Sakai, M. Nagao, and S. Fujibayashi, “Line Extraction and Pattern Detection in a Photograph”, Pattern Recognition, 1969. 4) H. Rowley, S. Baluja, and T. Kanade, “Neural Network-Based Face Detection”, Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1996.
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