Deep Learning Approach to Face Pose Estimation for High-Speed Camera Network System
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Abstract:
Three-dimensional face pose estimation has been vastly researched in computer vision, as the face recognition techniques can be utilized in tremendous applications not only regarding human behavior monitoring but also about human-computer interaction. In this paper, we attempted to build a deep-learning model which classifies the pan angle of human head by directly applying convolutional neural network without preliminary image processing, for low-resolution face images. In comparison with the transfer learnings based on pre-trained model, customized simple model consisting of a few convolutional layers and dropout scheme showed an enhanced accuracy in face pan angle prediction.Keywords:
Dropout (neural networks)
Transfer of learning
This paper studies the application and realization of face recognition in remote intelligent monitoring system. First, it introduces the development and importance of remote intelligent monitoring system, and then understands the steps and principles of face recognition. Subsequently, an embedded intelligent video surveillance system architecture is proposed, which combines face detection, face recognition, feature matching and other technologies to complete the recognition of the target face in the surveillance system. Finally, the Adaboost face detection algorithm is introduced, which greatly reduces the detection time and the amount of data that needs to be calculated. The feasibility of the algorithm is verified through experimental simulations.
AdaBoost
Object-class detection
Feature (linguistics)
Realization (probability)
Three-dimensional face recognition
Template matching
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The most immediate and quickest method of human-computer interaction, which is the trend of the development of robots, is interacting with robots with the expression of human beings. (To realize this function, a face recognition system is necessary.) A face recognition system includes several parts, such as face detection, skin color detection, image processing, and so on. Two main methods of face recognition are introduced in this paper: bi-linear interpolation and improved linear discriminant analysis. What is performed at the end of the paper is an experimental research and analysis of the influence that lighting changes have on recognition rate.
Three-dimensional face recognition
Interpolation
Object-class detection
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Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back‐dropout transfer learning (NB‐TL), which utilizes images that have been misclassified and further performs back‐dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB‐TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.
Dropout (neural networks)
Transfer of learning
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Abstract Face recognition is a method for recognizing human faces using a camera. There are various applications of facial recognition technology, one of which is Face Unlock technology on smartphones which functions as a security feature to open smartphone access through the user’s face. In this study, the use of facial recognition technology is used to automatically open the door of the room according to the registered face. The research method used is the waterfall method which has 5 stages. The research stages consist of requirements analysis, design, implementation & unit testing, integration & system testing, and operation & maintenance. This study uses a Raspberry Pi 4 to perform an automation system. The face detection process is based on the YuNet detection model, as well as the face recognition process using the SFace facial recognition model.
Lock (firearm)
Three-dimensional face recognition
Feature (linguistics)
Feature recognition
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Within a human's lifetime, faces are the visually embellished images that appear most frequently. Facial Recognition is the ability to recognize and discover someone primarily based totally on their facial features. Because the face is multidimensional, it necessitates several mathematical calculations. Thus, this work is aimed at developing an improved face recognition system that will serve in numerous application areas. Many approaches have already been developed, but they have low recognition capabilities and a high false alarm rate. As a result, there is a need for face recognition system that is more accurate and has a shorter recognition time. Due to rising security concerns and the quick development of mobile devices, face detection has recently been a hot research area. This paper focuses on the implementation of a face recognition system for human identification based on the open-source computer vision library (OpenCV) with python.At last the current application areas related to face recognition system is represented.
Python
Three-dimensional face recognition
3D single-object recognition
Object-class detection
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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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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
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Citations (9)
A face recognition program can be an innovation that can recognize or check an individual from an advanced photograph or video outline from a video source. It is used to find the world safer and more user-oriented. Finding missing persons, retail crime, security identification, identifying accounts on social media systems, or recognition of the drivers of cars are just a few examples. Face recognition is categorized into three steps: face detection, face extraction and face identification. In this work, we create a face recognition application platform using Open-Computer- Vision (OpenCV), with a focus on the system's performance and accuracy. Haar-Cascade was used for the Face Detection, Eigenfaces, Fisherfaces and Local binary pattern histograms were being used for Face or Facial Recognition. After the testing is completed, System is proved to have a friendly interface and it can detect and identify faces in real-time and accurately.
Eigenface
Three-dimensional face recognition
Local Binary Patterns
Haar-like features
Object-class detection
Identification
SAFER
3D single-object recognition
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Citations (6)
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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Citations (1)