Implementation of a specified face recognition system based on video
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Face recognition has been an important research direction in computer vision for a long time. The algorithms proposed in related fields are endless, and the accuracy that can be achieved is higher than ever. However, it is still quite difficult to apply face recognition technology. This paper combines the algorithms of face detection and face recognition to build a video-based face recognition system for efficient and accurate recognition of the faces of specified characters in the video. The system extracts the image of the person to be detected through MTCNN, and then uses Facenet to extract its features. Finally, the images are classified by SVM, thereby detecting the person to be detected appearing in the video. Experiments show that our method can still achieve good recognition results when the target is lacking data and the picture quality of the video is unstable. The accuracy of our method in self-built dataset in this paper can reach 94.9%.Keywords:
Three-dimensional face recognition
Object-class detection
Face detection is an essential first step in face recognition systems with the purpose of localizing and extracting the face region from the background. Apart from increasing the efficiency of face recognition systems, face detection technique also opens up the door of opportunity for application areas such as content based image retrieval, video encoding, video conferencing, crowd surveillance and intelligent human computer interfaces. In this paper, we propose a new face detection approach which is capable of detecting human faces from complex backgrounds. A skin color modeling process is proposed to the face segmentation process. Image enhancement is then used to improve the features of face candidates before feeding to the face object classifier which is based on the modified Hausdorff distance. The overall performance of the face detection system is evaluated with a successful rate of 87.5%.
Object-class detection
Three-dimensional face recognition
Hausdorff distance
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Object-class detection
Three-dimensional face recognition
Face hallucination
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Abstract: The face is one of the easiest way to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Now a days Human Face Detection and Recognition become a major field of interest in current research because there is no deterministic algorithm to find faces in a given image. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is recognition, which recognize (by comparing face with picture or either with image captured through webcam) a face as an individual. In face detection and recognition technology, it is mainly introduced from the OpenCV method. Face recognition is one of the much-studied biometrics technology and developed by experts. The area of this project face detection system with face recognition is Image processing. The software requirement for this project is Python. Keywords: face detection, face recognition, cascade_classifier, LBPH.
Object-class detection
Three-dimensional face recognition
3D single-object recognition
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Abstract : This report describes research efforts towards developing algorithms for a robust face recognition system to overcome many of the limitations found in existing two-dimensional facial recognition systems. Specifically, the report addresses the problem of detecting faces in color images in the presence of various lighting conditions and complex backgrounds as well as recognizing faces under variations in pose, lighting, and expression. The report is organized in two main parts: face detection and face recognition. A near real-time face detection system was developed that uses a skin-tone color model and facial features. For face recognition, the authors have developed four independent solutions: (1) evidence accumulation for 2D face recognition, (2) demographic information extraction from 2D facial images, (3) 3D-model enhanced 2D face recognition with a small number of training samples, and (4) 3D face recognition.
Three-dimensional face recognition
Face hallucination
Object-class detection
3D single-object recognition
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Human and computer vision has a vital role in intelligent interaction with computer, face recognition is one of the subjects that have a wide area in researches, a big effort has been exerted in last decades for face recognition, face detection, face tracking, as yet new algorithms for building fully automated system are required, these algorithms should be robust and efficient. The first step of any face recognition system is face detection, the goal of face detection is the extraction of face region within image, taking into consideration lightning, orientation and pose variation, whenever this step accurate the result of face recognition will be better, this paper introduce a survey of techniques and methods of feature based face detection.
Object-class detection
Three-dimensional face recognition
Feature (linguistics)
Facial motion capture
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The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition isa personal identification system that uses personal characteristics of a person to identify the person's identity.Human face recognition procedure basically consists of two phases, namely face detection, where this process takesplace very rapidly in humans, except under conditions where the object is located at a short distance away, the nextis the introduction, which recognizes a face as individuals. Stage is then replicated and developed as a model forfacial image recognition (face recognition) is one of the much-studied biometrics technology and developed byexperts. There are two kinds of methods that are currently popular in developed face recognition patterns, namely,the Eigenface method and Fisherface method. Facial image recognition Eigenface method is based on thereduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The mainpurpose of the use of PCA on face recognition using Eigenfaces was formed (face space) by finding the eigenvectorcorresponding to the largest eigenvalue of the face image. The area of this project's face detection system with facerecognition is Image processing. The software requirements for this project is matlab software.
Eigenface
Three-dimensional face recognition
Object-class detection
3D single-object recognition
Face hallucination
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With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research.
Object-class detection
Three-dimensional face recognition
Benchmark (surveying)
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With the continuous development of computer, many advanced technologies are mostly based on computer vision, face recognition technology plays an important role. The camera captures the image or video stream containing the human face, and detects and tracks the face in the image. This series of related technologies are also called face detection and recognition. The method of face recognition is more natural, more intuitive, and has the characteristics of indirect and concurrency. It has been widely used in many fields. Now, computer vision is an active research field. In this paper, we study the methods of face detection and recognition in the field of computer vision. The user interface is designed with PyQt, and the function of face model training, reading the image to be recognized, extracting the image to be recognized and the recognition of human face are realized. The experimental results show that the proposed method has high recognition efficiency, and a complete algorithm system based on face tracking detection and face recognition is realized, which can lay a good foundation for future research on the aspect of vision.
Three-dimensional face recognition
Object-class detection
3D single-object recognition
Facial motion capture
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Most doors are controlled by persons using keys, security cards, passwords, or patterns to open the door. This paper aims to help users improve the door security of sensitive locations by using face detection and Recognition. The face is a complex multidimensional structure and needs good computing techniques for detection and Recognition. This paper comprises three subsystems: face detection, face Recognition and automatic door access control. Face detection is the process of detecting the region of the face in an image. The look is seen using the viola jones method, and face recognition is implemented using the Principal Component Analysis (PCA). Face Recognition based on PCA is generally referred to as the use of Eigenfaces. If a face is recognized, it is known, else it is unknown. The door will open automatically for the known person due to the command of the microcontroller. On the other hand, the alarm will ring for the unknown person. Since PCA reduces the dimensions of face images without losing essential features, facial images for many persons can be stored in the database. Although many training images are used, computational efficiency cannot be decreased significantly. Therefore, face recognition using PCA can be more beneficial for door security systems than other face recognition schemes.
Eigenface
Three-dimensional face recognition
Object-class detection
Doors
Lock (firearm)
3D single-object recognition
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