Face Occlusion Detection Using Skin Color Ratio and LBP Features for Intelligent Video Surveillance Systems

Ji, Pengfei; Kim, Yonghwa; Yang, Yue-Tzu

doi:10.15439/2016f508

Cited by 15 publications

(2 citation statements)

References 23 publications

(29 reference statements)

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“…In [22] Authors have focused on an approach for improvementation in the speed of detecting faces with different ways like processing image pixels concurrently optimised memory transfer between Central Processing Unit and Graphics Processing Unit. In [27] Support Vector Machine (SVM) is trained based on Local Binary Pattern feature. The recognition result of SVM and the result of skin-color ratio feature are merged by weighted voting strategy, and then occluded faces are classified as concealed, partially concealed, and visible.…”

Section: Literature Surveymentioning

confidence: 99%

Face Detection and Processing: a Survey

Kharat¹

2018

IJET

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In the recent era facial image processing is gaining more importance and the face detection from image or from video have number of applications which are video surveillance, entertainment, security, multimedia, communication, Ubiquitous computing etc. Various research work are carried out for face detection and processing which includes detection, tracking of the face, estimation of pose, clustering the detected faces etc. Although significant advances have been made, the performance of face detection systems provi de satisfactory under controlled environment & may get degraded with some challenging scenario such as in real time video face detection and processing. There are many real-time applications where human face serves as identity and these application are time bound so time for detection of face from image or video and the further processing is very essential, thus here our goal is to discuss the face detection system overview and to review various human skin colors based approaches and Haar feature based approach for better detection performance. Detected faces tagging and clustering is essential in some cases, so for such further processing time factor plays important role. Some of the recent approaches to improve detection speed such as using Graphical Processing Unit are discussed and providing future directions in this area.

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Section: Literature Surveymentioning

confidence: 99%

Face Detection and Processing: a Survey

Kharat¹

2018

IJET

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“…Position of the nose, space between the eyes and a few other things are the rules governing the process of face detection. There is a pre-defined manual standard sample which facilitates the process of temple matching [2] and then the search window is used to explore the other possible face areas. The factors like direction, size and rotation of the face can affect this otherwise simple method which we just mentioned.…”

Section: Introductionmentioning

confidence: 99%

Face Detection and Recognition Using Haar Classifier and LBP Histogram

Das¹,

Gangopadhyay²,

Chatterjee³

2018

ijarcs

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Abstract:Facial recognition technology is the process for identifying or verifying a face from digital images. The need for face recognition has been of real importance with the development of modern society. Detection and recognition of faces has been on the rise worldwide owing the requirement for security for economic transactions, authorization, national safety and security and other important factors. The technology comprises of face detection, database creation and face recognition. This paper presents a new approach of face identification using LBP method and Haar-like features. The first step is face detection which is done using Haar cascade classifier. After detection, a face is saved in the database. Then the faces from the database are passed through the face recognition algorithm. The Local Binary Pattern Histogram (LBPH) method is used for face recognition. The performance of face detection can be seen to produce maximum error of 1.6%, 2.1% and 0.8% in case of Real-Time video, image file and video file respectively which may be considered accurate. The recognition algorithm produces maximum error of 0.4% which may be considered accurate as well.

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A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

Siddiqui,

Siddiqi

2023

Communications in Computer and Information Science

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Face Occlusion Detection Using Skin Color Ratio and LBP Features for Intelligent Video Surveillance Systems

Cited by 15 publications

References 23 publications

Face Detection and Processing: a Survey

Face Detection and Processing: a Survey

Face Detection and Recognition Using Haar Classifier and LBP Histogram

A Novel Optimization Algorithm for Smart Video Surveillance System and Change Object Detection

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