{"id":28261718,"url":"https://github.com/arpandatta011/facial_image_recognition","last_synced_at":"2026-04-09T02:31:13.048Z","repository":{"id":164729682,"uuid":"494557079","full_name":"arpandatta011/Facial_Image_recognition","owner":"arpandatta011","description":"This project includes the introduction of the facial expression recognition and an investigation on the recent previous researches for extracting the effective and efficient method for facial expression recognition.","archived":false,"fork":false,"pushed_at":"2022-05-20T18:20:21.000Z","size":4588,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-03T18:48:13.326Z","etag":null,"topics":["cv2","keras","matplotlib","numpy","pandas","tensorflow"],"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/arpandatta011.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}},"created_at":"2022-05-20T17:44:18.000Z","updated_at":"2023-04-16T20:50:58.000Z","dependencies_parsed_at":null,"dependency_job_id":"eabb6a9c-4143-421b-aa7a-8b2cdbd796c7","html_url":"https://github.com/arpandatta011/Facial_Image_recognition","commit_stats":null,"previous_names":["arpandatta011/facial_image_recognition"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/arpandatta011/Facial_Image_recognition","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arpandatta011%2FFacial_Image_recognition","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arpandatta011%2FFacial_Image_recognition/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arpandatta011%2FFacial_Image_recognition/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arpandatta011%2FFacial_Image_recognition/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arpandatta011","download_url":"https://codeload.github.com/arpandatta011/Facial_Image_recognition/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arpandatta011%2FFacial_Image_recognition/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31582567,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"online","status_checked_at":"2026-04-09T02:00:06.848Z","response_time":112,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["cv2","keras","matplotlib","numpy","pandas","tensorflow"],"created_at":"2025-05-20T06:11:43.786Z","updated_at":"2026-04-09T02:31:13.029Z","avatar_url":"https://github.com/arpandatta011.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Facial_Image_recognition 🧑‍🦰👩\n\n![](https://imgopt.infoq.com/fit-in/1200x2400/filters:quality(80)/filters:no_upscale()/news/2018/10/EmoPy-Computer-Vision/en/resources/1emotions-1540772527761.png)\n\nHuman facial expressions can be easily classified into 7 basic emotions: happy, \nsad, surprise, fear, anger, disgust, and neutral. Our facial emotions are \nexpressed through activation of specific sets of facial muscles. These \nsometimes subtle, yet complex, signals in an expression often contain an \nabundant amount of information about our state of mind. Through facial \nemotion recognition, we are able to measure the effects that content and \nservices have on the audience/users through an easy and low-cost procedure. \nFor example, retailers may use these metrics to evaluate customer interest. \nHealthcare providers can provide better service by using additional information \nabout patients' emotional state during treatment. Entertainment producers can \nmonitor audience engagement in events to consistently create desired content.\nHumans are well-trained in reading the emotions of others, in fact, at just 14 \nmonths old, babies can already tell the difference between happy and sad. But \ncan computers do a better job than us in accessing emotional states? To answer \nthe question, We designed a deep learning neural network that gives machines \nthe ability to make inferences about our emotional states. In other words, we \ngive them eyes to see what we can see.\n\n\n### About the Dataset \n\nThe dataset, used for training the model is from a Kaggle Facial Expression \necognition Challenge a few years back (FER2013). The data consists of 48x48 \npixel grayscale images of faces. The faces have been automatically registered \nso that the face is more or less centered and occupies about the same amount of \nspace in each image. The task is to categorize each face based on the emotion \nshown in the facial expression in to one of seven categories (0=Angry, \n1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).\nThe training set consists of 28,709 examples. The public test set used for the \nleaderboard consists of 3,589 examples. The final test set, which was used to \ndetermine the winner of the competition, consists of another 3,589 examples. \nEmotion labels in the dataset:\n0: -4593 images- Angry\n1: -547 images- Disgust\n2: -5121 images- Fear\n3: -8989 images- Happy\n4: -6077 images- Sad\n5: -4002 images- Surprise\n6: -6198 images- Neutral\n\n### CONCLUSION 💡\n\nThe facial expression recognition system presented in this research work \ncontributes a resilient face recognition model based on the mapping of \nbehavioral characteristics with the physiological biometric characteristics. The \nphysiological characteristics of the human face with relevance to various \nexpressions such as happiness, sadness, fear, anger, surprise and disgust are \nassociated with geometrical structures which restored as base matching emplate \nfor the recognition system.The behavioral aspect of this system relates the \nattitude behind different expressions as property base. The property bases are \nalienated as exposed and hidden category in genetic algorithmic genes. The \ngene training set evaluates the expressional uniqueness of individual faces and \nprovide a resilient expressional recognition model in the field of biometric \nsecurity. The design of a novel asymmetric cryptosystem based on biometrics \nhaving features like hierarchical group security eliminates the use of passwords \nand smart cards as opposed to earlier cryptosystems. It requires a special\nhardware support like all other biometrics system. This research work promises \na new direction of research in the field of asymmetric biometric cryptosystems \nwhich is highly desirable in order to get rid of passwords and smart cards \ncompletely. Experimental analysis and study show that the hierarchical security \nstructures are effective in geometric shape identification for physiological\ntraits\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farpandatta011%2Ffacial_image_recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farpandatta011%2Ffacial_image_recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farpandatta011%2Ffacial_image_recognition/lists"}