{"url":"/dataset/ck","name":"CK+","full_name":"Extended Cohn-Kanade dataset","description_markdown":"The Extended Cohn-Kanade (**CK+**) dataset contains 593 video sequences from a total of 123 different subjects, ranging from 18 to 50 years of age with a variety of genders and heritage. Each video shows a facial shift from the neutral expression to a targeted peak expression, recorded at 30 frames per second (FPS) with a resolution of either 640x490 or 640x480 pixels. Out of these videos, 327 are labelled with one of seven expression classes: anger, contempt, disgust, fear, happiness, sadness, and surprise. The CK+ database is widely regarded as the most extensively used laboratory-controlled facial expression classification database available, and is used in the majority of facial expression classification methods.\r\n\r\nSource: [EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-time Facial Expression Recognition](https://arxiv.org/abs/2006.15759)","description_withheld":null,"homepage":"http://www.jeffcohn.net/Resources/","introduced_date":"2010-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression","first_author":null,"url":"https://doi.org/10.1109/CVPRW.2010.5543262"},"license":{"name":"Custom (non-commercial)","url":"http://www.jeffcohn.net/wp-content/uploads/2020/10/2020.10.26_CK-AgreementForm.pdf100.pdf.pdf"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Facial Expression Recognition (FER)","url":"/task/facial-expression-recognition","datasets_with_task":"/datasets/task/facial-expression-recognition"},{"name":"Face Verification","url":"/task/face-verification","datasets_with_task":"/datasets/task/face-verification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CK+"],"data_loaders":[{"repo":"https://github.com/users/KCN77","url":"https://github.com/users/KCN77","frameworks":["tf","pytorch"]}],"num_papers_in_archive":238,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-expression-recognition-on-ck","task":"Facial Expression Recognition (FER)","dataset_variant":"CK+","rows":7,"metrics":["Accuracy (8 emotion)","Accuracy (7 emotion)","Accuracy (6 emotion)"],"first_row_in_archive_order":{"model":"EmoNeXt","paper":"/paper/a-novel-deep-learning-approach-for-facial","metrics":{"Accuracy (8 emotion)":"100"},"code_links":[{"title":"yelboudouri/EmoNeXt","url":"https://github.com/yelboudouri/EmoNeXt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-verification-on-ck","task":"Face Verification","dataset_variant":"CK+","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SphereFace","paper":"/paper/sphereface-deep-hypersphere-embedding-for","metrics":{"Accuracy":"93.80"},"code_links":[{"title":"wy1iu/sphereface","url":"https://github.com/wy1iu/sphereface"},{"title":"clcarwin/sphereface_pytorch","url":"https://github.com/clcarwin/sphereface_pytorch"},{"title":"cvqluu/Additive-Margin-Softmax-Loss-Pytorch","url":"https://github.com/cvqluu/Additive-Margin-Softmax-Loss-Pytorch"},{"title":"cvqluu/Angular-Penalty-Softmax-Losses-Pytorch","url":"https://github.com/cvqluu/Angular-Penalty-Softmax-Losses-Pytorch"},{"title":"Faceplugin-ltd/FaceRecognition-Android","url":"https://github.com/Faceplugin-ltd/FaceRecognition-Android"},{"title":"FaceOnLive/Face-Recognition-SDK-Android","url":"https://github.com/FaceOnLive/Face-Recognition-SDK-Android"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/sphereface"},{"title":"Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection","url":"https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection"},{"title":"RocketFlash/easy_metric_learning","url":"https://github.com/RocketFlash/easy_metric_learning/tree/master/tools"},{"title":"zuoqing1988/mobile-sphereface-caffe","url":"https://github.com/zuoqing1988/mobile-sphereface-caffe"},{"title":"sevenHsu/Face_Recognition_IN_Video","url":"https://github.com/sevenHsu/Face_Recognition_IN_Video"},{"title":"vohoaiviet/sphereface","url":"https://github.com/vohoaiviet/sphereface"},{"title":"Armxyz1/Results-on-RFW","url":"https://github.com/Armxyz1/Results-on-RFW"},{"title":"clcarwin/sphereface","url":"https://github.com/clcarwin/sphereface"},{"title":"code-implementation1/Code8","url":"https://github.com/code-implementation1/Code8/tree/main/sphereface"},{"title":"2023-MindSpore-4/Code11","url":"https://github.com/2023-MindSpore-4/Code11/tree/main/sphereface"},{"title":"2023-MindSpore-4/Code7","url":"https://github.com/2023-MindSpore-4/Code7/tree/main/squeezenet"},{"title":"2023-MindSpore-4/Code7","url":"https://github.com/2023-MindSpore-4/Code7/tree/main/sphereface"},{"title":"alililia/ascend_sphereface","url":"https://github.com/alililia/ascend_sphereface"},{"title":"alililia/gpu_sphereface","url":"https://github.com/alililia/gpu_sphereface"},{"title":"yangyucheng000/sphereface","url":"https://github.com/yangyucheng000/sphereface"},{"title":"vnbot2/arcface","url":"https://github.com/vnbot2/arcface"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-novel-deep-learning-approach-for-facial","title":"A novel deep learning approach for facial emotion recognition: application to detecting emotional responses in elderly individuals with Alzheimer’s disease","date":"2024-12-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/patt-lite-lightweight-patch-and-attention","title":"PAtt-Lite: Lightweight Patch and Attention MobileNet for Challenging Facial Expression Recognition","date":"2023-06-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-vision-transformer-with-squeeze-and","title":"Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition","date":"2021-07-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/using-self-supervised-co-training-to-improve","title":"Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation","date":"2021-05-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/frame-attention-networks-for-facial","title":"Frame attention networks for facial expression recognition in videos","date":"2019-06-29","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/deep-emotion-facial-expression-recognition","title":"Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network","date":"2019-02-04","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/sphereface-deep-hypersphere-embedding-for","title":"SphereFace: Deep Hypersphere Embedding for Face Recognition","date":"2017-04-26","rows_on_this_dataset":1,"code_links":22,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/facenet2expnet-regularizing-a-deep-face","title":"FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition","date":"2016-09-21","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}