scholarly journals Smart Surveillance System using Face Tracking

2021 ◽  
Vol 9 (VI) ◽  
pp. 2613-2617
Author(s):  
Priyanka Agrawal
Keyword(s):  
Face Detection ◽  
Criminal Activity ◽  
Video Tracking ◽  
Face Tracking ◽  
Surveillance Systems ◽  
Video Analytics ◽  
Detection And Tracking ◽  
The Face ◽  
Video Input ◽  
Human Faces

The face is seen as a key component of the human body, and humans utilise it to identify one another. Face detection in video refers to the process of detecting a person's face from a video sequence, while face tracking refers to the process of tracking the person's face throughout the video. Face detection and tracking has become a widely researched issue due to applications such as video surveillance systems and identifying criminal activity. However, working with videos is tough due to problems such as bad illumination, low resolution, and atypical posture, among others. It is critical to produce a fair analysis of various tracking and detection strategies in order to fulfil the goal of video tracking and detection. Closed-circuit television (CCTV) technology had a significant impact on how crimes were investigated and solved. The material used to review crime scenes was CCTV footage. CCTV systems, on the other hand, just offer footage and do not have the ability to analyse it. In this research, we propose a system that can be integrated with the CCTV footage or any other video input like webcam to detect, recognise, and track a person of interest. Our system will follow people as they move through a space and will be able to detect and recognise human faces. It enables video analytics, allowing existing cameras to be combined with a system that will recognise individuals and track their activities over time. It may be used for remote surveillance and can be integrated into video analytics software and CCTV security solutions as a component. It may be used on college campuses, in offices, and in shopping malls, among other places.

scholarly journals Computerized Face Detection and Tracking

2019 ◽  
Vol 8 (12) ◽  
pp. 2800-2802 ◽  
Keyword(s):  
Face Detection ◽  
Basic Research ◽  
Joint Effort ◽  
The Past ◽  
Face Area ◽  
Detection And Tracking ◽  
Target Article ◽  
The Face ◽  
Testing Issue ◽  
Human Faces

Item revelation has been an unprecedented test from the past couple of years. In PC vision human face area is a basic research topic. It is required for some PC applications like HCI, perception, human-robot joint effort, etc. In this field facial following is finding greater advancement of use in security and prosperity applications to recognize distinctive situations1 . This following zone can be used to control or talk with robots. Perceiving human faces in a video is a fantastic testing issue. These setups may take after edge of view, establishment control, and diverse edifications. This is a result of high variety of setups that may occur. The multifaceted idea of the face results in a particular dimension of issue for speedy area and following. In this paper we use two philosophies for recognizing a face and track it always. In a general sense video groupings give a larger number of information than a still picture. It is reliably a testing undertaking to pursue a target thing in a live video. We experience challenges like edification; present assortment and hindrance in pre-planning stages. In any case, this is can be overpowered by disclosure of the target article unendingly in each and every edge. Face following by Kanade Lucas Tomasi computation that is used to pursue face reliant on arranged features.Automatic face detection and tracking is a challenging part and here the proposed method is improvised on that.

Face and Eye Detection

2011 ◽  
pp. 5-44 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Facial Expression Recognition ◽  
Face Image ◽  
Face Tracking ◽  
Expression Recognition ◽  
Human Face ◽  
Eye Detection ◽  
Face Image Analysis ◽  
Face Images ◽  
The Face

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

Face Tracking

2011 ◽  
pp. 92-162
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Image Sequence ◽  
Face Tracking ◽  
Detection Methods ◽  
Continuous Change ◽  
Image Frame ◽  
The Face ◽  
Previous Image

When we want to analyze the continuous change of the face in an image sequence, applying face tracking methods is a better choice than applying the face detection methods to each image frame. Usually, the face tracking methods are more efficient than the ordinary face detection methods because they can utilize the trajectory of the face in the previous image frames with an assumption that the shape, texture, or motion of the face change smoothly. There have been many approaches to face tracking. We divide the face tracking methods into several categories according to the cues that are extracted for tracking.

ADDRESSING THE PROBLEMS OF DETECTING FACES WITH NEURAL NETWORKS

2007 ◽  
Vol 07 (04) ◽  
pp. 617-640 ◽  
Author(s):  
KEVIN CURRAN ◽  
NEIL McCAUGHLEY ◽  
XUELONG LI
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Pedestrian Detection ◽  
Image Sequences ◽  
Database Management System ◽  
Surveillance Systems ◽  
Human Computer Interface ◽  
Human Machine Interaction ◽  
The Face ◽  
Machine Interaction

The face is the most distinctive and widely used key to a person's identity. The area of face detection has attracted considerable attention in the advancement of human-machine interaction as it provides a natural and efficient way to communicate between humans and machines. The problem of facial parts in image sequences has become a popular area of research due to emerging applications in intelligent human-computer interface, surveillance systems, content-based image retrieval, video conferencing, financial transaction, forensic applications, pedestrian detection, image database management system and so on. This paper presents the results of an image based neural network face detection system which seeks to address the problem of detecting faces under gross variations.

scholarly journals A smart login system using face detection and recognition by ORB algorithm

2020 ◽  
Vol 20 (2) ◽  
pp. 1078
Author(s):  
Mohammad Jahangir Alam ◽  
Tanjia Chowdhury ◽  
Md. Shahzahan Ali
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Punch Card ◽  
Effective Technique ◽  
Face Images ◽  
Different Types ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Human Faces

<p>We can identify human faces using a web Camera which is known as Face Detection.  This is a very effective technique in computer technology. There are used different types of attendance systems such as log in with the password, punch card, fingerprint, etc. In this research, we have introduced a facial recognition type of biometric system that can identify a specific face by analyzing and comparing patterns of a digital image.  This system is the latest login system based on face detection. Primarily, the device captures the face images and stores the captured images into the specific path of the computer relating the information into a database. When any body tries to enter into any room or premises through this login system, the system captures the image of that particular person and matches the image with the stored image. If this image matches with the stored image then the system allows the person to enter the room or premises, otherwise the system denies entry. This face recognition login system is very effective, reliable and secured. This research has used the Viola and Jones algorithm for face detection and ORB for image matching in face recognition and Java, MySql, OpenCV, and iReport are used for implementation.</p>

scholarly journals A NOVEL FACE DETECTION AND TRACKING ALGORITHM IN REAL- TIME VIDEO SEQUENCES

2013 ◽  
pp. 25-29
Author(s):  
NAGAPRIYA KAMATH K ◽  
ASHWINI HOLLA ◽  
SUBRAMANYA BHAT
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Template Matching ◽  
Digital Images ◽  
Color Space ◽  
Color Model ◽  
Hsv Color Space ◽  
Detection And Tracking ◽  
Skin Color Model ◽  
Human Faces

Face detection is a image processing technology that determines the location and size of human faces in digital images or video. This module precedes face recognition systems that plays an important role in applications such as video surveillance, human computer interaction and so on. This proposed work focuses mainly on multiple face detection technique, taking into account the variations in digital images or video such as face pose, appearances and illumination. The work is based on skin color model in YCbCr and HSV color space. First stage of this proposed method is to develop a skin color model and then applying the skin color segmentation in order to specify all skin regions in an image. Secondly, a template matching is done to assure that the segmented image does not contain any non-facial part. This algorithm works to be robust and efficient.

scholarly journals Application of Multiscale Facial Feature Manifold Learning Based on VGG-16

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Huilin Ge ◽  
Zhiyu Zhu ◽  
Runbang Liu ◽  
Xuedong Wu
Keyword(s):  
Face Detection ◽  
Recognition Rate ◽  
Facial Feature ◽  
Time Dimension ◽  
Running Time ◽  
The Public ◽  
Detection And Tracking ◽  
Face Features ◽  
Face Target ◽  
The Face

Purpose. In order to solve the problems of small face image samples, high size, low structure, no label, and difficulty in tracking and recapture in security videos, we propose a popular multiscale facial feature manifold (MSFFM) algorithm based on VGG16. Method. We first build the VGG16 architecture to obtain face features at different scales and construct a multiscale face feature manifold with face features at different scales as dimensions. At the same time, the recognition rate, accuracy rate, and running time are used to evaluate the performance of VGG16, LeNet-5, and DenseNet on the same database. Results. From the results of comparative experiments, it can be seen that the recognition rate and accuracy of VGG16 are the highest among the three networks. The recognition rate of VGG16 is 97.588%, and the accuracy is 95.889%. And the running time is only 3.5 seconds, which is 72.727% faster than LeNet-5 and 66.666% faster than DenseNet. Conclusion. The model proposed in this paper breaks through the key problem in the face detection and tracking problem in the public security field, predicts the position of the face target image in the time dimension manifold space, and improves the efficiency of face detection.

Human Face Detection and Tracking Using RetinaFace Network for Surveillance Systems

2021 ◽  
Author(s):  
Moh. Edi Wibowo ◽  
Ahmad Ashari ◽  
Ardacandra Subiantoro ◽  
Wahyono Wahyono
Keyword(s):  
Face Detection ◽  
Surveillance Systems ◽  
Human Face ◽  
Detection And Tracking ◽  
Human Face Detection

scholarly journals Building Face Detection with Face Divine Proportions

2021 ◽  
Vol 17 (04) ◽  
pp. 63
Author(s):  
Mohamed Oualla ◽  
Khalid Ounachad ◽  
Abdelalim Sadiq
Keyword(s):  
Face Detection ◽  
Face Image ◽  
Integral Image ◽  
Detection Rates ◽  
Aesthetic Medicine ◽  
Adaboost Algorithm ◽  
The World ◽  
High Detection ◽  
The Face ◽  
Human Faces

<p class="0abstract"><span lang="EN-US">In this paper, we proposed an algorithm for detecting multiple human faces in an image based on haar-like features to represent the invariant characteristics of a face. The choice of relevant and more representative features is based on the divine proportions of a face. This technique, widely used in the world of beauty, especially in aesthetic medicine, allows the face to be divided into a set of specific regions according to known mathematical measures. Then we used the Adaboost algorithm for the learning phase. All of our work is based on the Viola and Jones algorithm, in particular their innovative technique called Integral Image, which calculates the value of a Haar-Like feature extracted from a face image. In the rest of this article, we will show that our approach is promising and can achieve high detection rates of up to 99%.</span></p>

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