{"id":16420196,"url":"https://github.com/geekquad/facial-recognition-with-pca","last_synced_at":"2026-05-15T17:07:49.674Z","repository":{"id":110635462,"uuid":"279548241","full_name":"geekquad/Facial-Recognition-with-PCA","owner":"geekquad","description":"Face Recognition Implementation using PCA, eigenfaces, and SVM","archived":false,"fork":false,"pushed_at":"2020-07-15T21:11:38.000Z","size":494,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-24T16:16:59.464Z","etag":null,"topics":["eigenfaces","face-recognition","gridsearchcv","lfw-dataset","pca","sklearn","svm-classifier"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/geekquad.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-07-14T09:59:19.000Z","updated_at":"2023-05-09T15:58:02.000Z","dependencies_parsed_at":"2023-04-01T09:18:05.154Z","dependency_job_id":null,"html_url":"https://github.com/geekquad/Facial-Recognition-with-PCA","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/geekquad/Facial-Recognition-with-PCA","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/geekquad%2FFacial-Recognition-with-PCA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/geekquad%2FFacial-Recognition-with-PCA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/geekquad%2FFacial-Recognition-with-PCA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/geekquad%2FFacial-Recognition-with-PCA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/geekquad","download_url":"https://codeload.github.com/geekquad/Facial-Recognition-with-PCA/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/geekquad%2FFacial-Recognition-with-PCA/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33072995,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-15T11:35:32.926Z","status":"ssl_error","status_checked_at":"2026-05-15T11:35:31.362Z","response_time":103,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["eigenfaces","face-recognition","gridsearchcv","lfw-dataset","pca","sklearn","svm-classifier"],"created_at":"2024-10-11T07:27:12.765Z","updated_at":"2026-05-15T17:07:49.659Z","avatar_url":"https://github.com/geekquad.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## PCA\nPCA is a systemized way to transform input features into principle components, then those principle components are used as **new features.**\nIt is basiclly a mathematical technique used for dimensionality reduction.\n\n### Working:\nPCA finds a new coordinate system that is obtained from the old one by translation and rotation only. and it moves the center of the coordinate sysetm with the centre of the data.\n##### PCA is resolved as:\n\u003cimg src=\"https://alexhwilliams.info/itsneuronalblog/img/pca/pca_two_views.png\" width=\"1000\" height=\"500\" \u003e\n\u003cimg src=\"https://liorpachter.files.wordpress.com/2014/05/pca_figure1.jpg\" width=\"500\" height=\"500\"\u003e\n\nThe amount of information we loose is equal to the distance between a given point(as it existed in the sort od 2D space) and its new spot in the line. \n\n#### Documentation:\n\n\u003ca href=\"https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html\"\u003e https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html \u003c/a\u003e\n\u003chr\u003e \u003c/hr\u003e\n\n## Facial Recognition using PCA:\nOne of the simplest and most effective PCA approaches used\nin face recognition systems is the so-called eigenface\napproach. This approach transforms faces into a small set of\nessential characteristics, eigenfaces, which are the main\ncomponents of the initial set of learning images (training set).\nRecognition is done by projecting a new image in the\neigenface subspace, after which the person is classified by\ncomparing its position in eigenface space with the position of\nknown individuals. \n\n##### Dataset: \u003ca href=\"https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html\"\u003e https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html \u003c/a\u003e\n\n### Evaluation:\n**Classification Report:**\n \n \n                    precision    recall  f1-score   support\n\n    Ariel Sharon      0.60      0.69      0.64        13\n    Colin Powell      0.78      0.88      0.83        60\n    Donald Rumsfeld   0.73      0.70      0.72        27\n    George W Bush     0.91      0.88      0.90       146\n    Gerhard Schroeder 0.81      0.88      0.85        25\n    Hugo Chavez       0.73      0.53      0.62        15\n    Tony Blair        0.91      0.86      0.89        36\n\n    accuracy                              0.84       322\n    macro avg         0.78      0.78      0.78       322\n    weighted avg      0.84      0.84      0.84       322\n    \n## Prediction:\n\n**(a) The most significant faces:** \n\n\u003cimg src=\"https://github.com/geekquad/Facial-Recognition-with-PCA/blob/master/signi.png\"\u003e\n\n**(b) Eigenfaces:**\n\n\u003cimg src=\"https://github.com/geekquad/Facial-Recognition-with-PCA/blob/master/eigenfaces.png\"\u003e\n\n\n    \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeekquad%2Ffacial-recognition-with-pca","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgeekquad%2Ffacial-recognition-with-pca","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeekquad%2Ffacial-recognition-with-pca/lists"}