Real Time Face Detection
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Face detection and recognition involves using automated processes to identify human faces in digital images and video in real-time. The goal is to build a system that can read images from a webcam, detect faces, and then recognize the faces by matching facial features to images stored in a database. However, the recognition rate of such a system may be lower since it can only recognize faces that are present in the limited database, and does not account for failures in face detection.
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In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
approaches are basically used for face recognition
methodologies. Eigen Faces, Fisher Faces and LBP
methodologies were introduced for recognition purpose.
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efficiencies. To observe the efficient techniques of facial
recognition, there are many scenarios to measure its
performance which are based on real time.
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Globally, the presence of biometrics is highly approachable to fix any hurdle and irrelevant input and make a secure and tangible environment. Indeed biometrics helps you tremendously. You can manage everything on your basis to compete in the market. Especially for the attendance services in any organization, office, and building, it is the most important thing to record the presence of someone.
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Sandeep Sharma presented on face recognition. He discussed the history and types of face recognition including 2D and 3D. He explained how face recognition works by measuring facial landmarks and using algorithms like PCA and LDA to analyze features. Challenges included disguises and large crowds. Future uses could include law enforcement, banking security, and airports. Advancements are still needed for widescale deployment.
Attendance system based on face recognition using python by Raihan Sikdar
The document discusses face recognition technology for use in an automatic attendance system. It first defines biometrics and face recognition, explaining that face recognition identifies individuals using facial features. It then covers how face recognition systems work by detecting nodal points on faces to create unique face prints. The document proposes using such a system to take student attendance in online classes during the pandemic, noting advantages like ease of use, increased security, and cost effectiveness. It provides examples of how the system would capture images, analyze features, and recognize enrolled students to record attendance automatically.
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Facial recognition is a form of biometric software that uses machine learning algorithms like Eigenfaces and Fisherfaces to analyze facial features from images and detect and identify faces. The technology extracts key facial feature data and stores it as a "faceprint" that can then be used to quickly verify someone's identity by matching a new image to the stored faceprint. While facial recognition provides benefits like improved security and identity verification, it also faces challenges like privacy concerns, inability to distinguish identical twins, and being affected by environmental factors. The future of facial recognition is promising as the technology continues to be applied in new areas like security and software access control.
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Real Time Face Detection
- 1. Real time Face Detection and Recognition
- 2. Face detection is an automated process being used in a variety of applications that identifies human faces in digital images. It is image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process.
- 3. Face Recognition: Our goal is to find the fundamentals for face recognition. We want to find a way to build a real-time face recognition system. We want to combine what we have learned in this class and come up with a more complex application.
- 4. The three components are to read image from webcam, face detection, and then lastly face recognition. The recognition rate of our program is lower as it only detects the faces present in the data base. Note, this recognition rate does not include the rate of failing the face detection.