This paper proposes face detection analysis for via CCTV surveillance the criminals. The experiments there are fifteen images in the database. The results, it was found that five times in the experiment both single face detection and group of face detection. The faulty image is caused by a slightly tilted face, tilted face or low light / night spot. An average of 35-55% of the face tilted beyond 90 degrees, the detection is not successful.

Face Detection Criminals through CCTV Cameras

This paper describes the Web Real-Time Communication (WebRTC) technology and the implementation of its clients and server. The main aim is to design and implement WebRTC video conferencing between browsers in real implementation using Chrome and (Wired & WiFi) of LAN & WAN networks. Also, an evaluation of CPU performance, bandwidth consumption and Quality of Experience (QoE) was achieved. Moreover, a signalling channel between browsers using the WebSocket protocol via Node.js platform has been created and executed. This paper will give web developer an opportunity to comprehend the WebRTC technology, as well as to understand how to design WebRTC video conferencing.

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Design and evaluation of browser-to-browser video conferencing in WebRTC

Face recognition is the biometric personal identification that gaining a lot of attention recently. This method has the ability to identify a person from still image and video by using human face. For the accurate recognition, algorithm and reference database needs to be concerned. However, in the practical system have many external factors that affect to the recognition accuracy differently for each algorithm. This is a challenge problem of class attendance recording system deployment, which has uncontrolled environments. This paper comparing three well known algorithm that are Eigenfaces, Fisherfaces, and LBPH by adopts our new database that contains a face of individuals with variety of pose and expression. The experiment of face recognition in video conducted by varied the external factors that are light exposure, noise, and the video resolution, in the possible range. The results showed LBPH got the highest accuracy in all experiments, but this algorithm has the higher impact of the negative light exposure and high noise level more than the others that are statistical approach.

An evaluation of face recognition algorithms and accuracy based on video in unconstrained factors

Face recognition is a personal identification method using biometrics that is gaining the attention in this research field. The face recognition process can be done without the human and devices interaction, so it can be applied in several applications. In additions, the face recognition systems are typically implemented at different places in unconstrained environments. Hence, the study of the factors that impact the face recognition accuracy is an interesting and challenging topic. In the class attendance checking system using face recognition, there are variations of three factors that possibly affect the accuracy of the system; facial expressions, and face viewpoints. This study intends to compare facial recognition accuracy of three well-known algorithms namely Eigenfaces, Fisherfaces, and LBPH. The experiments conducted in the respects of the variation of facial expressions, and face viewpoints in the actual classroom. The results of the experiment demonstrated that LBPH is the most precise algorithm which achieves 81.67% of accuracy in still-image-based testing. The facial expression that has the most impact on accuracy is the grin, and face viewpoints that affect accuracy are looking down and tilting left, and right respectively. Therefore, LBPH is the most suitable algorithm to apply in a class attendance checking system after considering the accuracy.

Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms

WebRTC (Web Real-Time Communication) is a technology that enables browser-to-browser communication. Therefore, a signalling mechanism must be negotiated to create a connection between peers. The main aim of this paper is to create and implement a WebRTC hybrid signalling mechanism named (WebNSM) for video conferencing based on the Socket.io (API) mechanism and Firefox. WebNSM was designed over a combination of different topologies, such as simplex, star and mesh. Therefore it offers several communications at the same time as one-to-one (unidirectional/bidirectional), one-to-many (unidirectional) and many-to-many (bi-directional) without any downloading or installation. In this paper, WebRTC video conferencing was accomplished via LAN and WAN networks, including the evaluation of resources in WebRTC like bandwidth consumption, CPU performance, memory usage, Quality of Experience (QoE) and maximum links and RTPs calculation. This paper presents a novel signalling mechanism among different users, devices and networks to offer multi-party video conferencing using various topologies at the same time, as well as other typical features such as using the same server, determining room initiator, keeping the communication active even if the initiator or another peer leaves, etc. This scenario highlights the limitations of resources and the use of different topologies for WebRTC video conferencing.

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Design and Implement a Hybrid WebRTC Signalling Mechanism for Unidirectional & Bi-directional Video Conferencing

This paper proposed the technical study of real-time face detection system and also cover a key technology. In order to find the most appropriate factors, techniques, and algorithm by evaluating the performance that included connection speed, and effectiveness of tracking and detecting a human face in various conditions. WebRTC worked perform in any condition of device and platform independence. Furthermore, WebRTC could operate securely on the HTTPS protocol. In the case of the transferring ability of images, it relied on the connection speed; that LAN and WiFi are the most prefer for this best image quality. The results shown that Haar-like feature and CLM have significantly detected the face area over the web browser in almost light conditions. However, Haar-like has the better precision when operated on the low-speed processor. The real-time face detection system could be identified the person who walked through the capture device. In addition, this proposed system could be applied to any scope of the personal identification system.

An evaluation of technical study and performance for real-time face detection using Web Real-Time Communication

In the classroom, students can get the most benefit for themselves when attend and participate in the classroom. Roll-call is a classical method that mostly uses for the class participation enrollment. The time that used for this method is depended on the number of students; the more number of students, the more time to spend. This work presents the method that improves the class participation enrollment process Thus, we developed the face detection and face recognition system by applying the WebRTC. Since it is a platform independent, we could capture the participant faces from anywhere without an installation. In addition, the three standard face detection and recognition algorithms were applied in two main processes properly. The result showed that system can improve the class participation enrollment accuracy to be more precise and persuaded the student to attend the class as well. Moreover, the system can install to the classroom easily because it is developed in form of the web application and needs an only web camera for the additional device.

A real-time face recognition for class participation enrollment system over WebRTC

Face recognition is one of the most popular research topics of the biometric identification. Sometimes, face recognition work under the unstable environments. Therefore, the study on the factors that affect face recognition performance is the very interesting topic. The recognition accuracy can be affected by many factors, but the quality of image database and system environmental factors, are mainly factors that need to be focused. This research creates the image database to represent each user by controlled the environmental factors strictly, to ensure that the images have minimum confounding and have the best quality. The performance of the processing was tested by the variation of factors. These are variant variables that using for testing the algorithms accuracy and processing speed. The standard processing times are done on the still images. In the video, the experiment was conducted by using the test set with variation of confounding factors. All factors are similar to the still images, but it has one addition factors that is video resolution. The results found that, the face recognition processing time, the factors that are face recognition algorithms, video resolution, and the number of input image affected to the processing speed. As results from the experiment, it is relevant for the hypothesis that is the smaller number of input image, and smaller image size can reduce the processing time. In the case of face recognition algorithm, Fisherfaces is the fastest algorithms which took a shorter processing time than Eigenfaces, and LBPH, respectively. The results from this experiment can indicate the most effective face recognition algorithm. After that, it would be applied for the as-is system.

Phichaya Jaturawat

Papers

An evaluation of technical study and performance for real-time face detection using Web Real-Time Communication

An evaluation of face recognition algorithms and accuracy based on video in unconstrained factors

Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms

A real-time face recognition for class participation enrollment system over WebRTC

Effect of variation factors on the processing time of the face recognition algorithms in video sequence