Facial Image Processing in Computer Vision

2011 ◽  
pp. 179-190
Author(s):  
Moi Hoon Yap ◽  
Hassan Ugail
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Animation ◽  
Feature Detection ◽  
Research Field ◽  
Future Research ◽  
Expression Recognition ◽  
Facial Image ◽  
Detection Techniques ◽  
Future Challenges

The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.

Facial Image Processing in Computer Vision

2013 ◽  
pp. 1111-1123
Author(s):  
Moi Hoon Yap ◽  
Hassan Ugail
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Animation ◽  
Feature Detection ◽  
Research Field ◽  
Future Research ◽  
Expression Recognition ◽  
Facial Image ◽  
Detection Techniques ◽  
Future Challenges

The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.

scholarly journals End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3691
Author(s):  
Ciprian Orhei ◽  
Silviu Vert ◽  
Muguras Mocofan ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Open Source ◽  
Visual Processing ◽  
Research Field ◽  
Learning Models ◽  
Research Activity ◽  
End To End ◽  
Machine Learning Models

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.

From Face to Facial Expression

2010 ◽  
pp. 217-238
Author(s):  
Zakia Hammal ◽  
Zakia Hammal
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Everyday Life ◽  
Facial Expression Recognition ◽  
Multidisciplinary Approach ◽  
Expression Recognition ◽  
Future Challenges ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Processing Steps

This chapter addresses recent advances in computer vision for facial expression classification. The authors present the different processing steps of the problem of automatic facial expression recognition. They describe the advances of each stage of the problem and review the future challenges towards the application of such systems to everyday life situations. The authors also introduce the importance of taking advantage of the human strategy by reviewing advances of research in psychology towards multidisciplinary approach for facial expression classification. Finally, the authors describe one contribution which aims at dealing with some of the discussed challenges.

A Review of Facial Feature Detection Algorithms

2010 ◽  
pp. 42-61
Author(s):  
Stylianos Asteriadis ◽  
Stylianos Asteriadis ◽  
Nikos Nikolaidis ◽  
Nikos Nikolaidis ◽  
Ioannis Pitas ◽  
...  
Keyword(s):  
Feature Detection ◽  
Performance Metrics ◽  
Facial Feature ◽  
Research Field ◽  
Head Pose Estimation ◽  
Expression Recognition ◽  
Facial Feature Detection ◽  
Detection Algorithms ◽  
Active Research ◽  
And Performance

Facial feature localization is an important task in numerous applications of face image analysis that include face recognition and verification, facial expression recognition, driver‘s alertness estimation, head pose estimation etc. Thus, the area has been a very active research field for many years and a multitude of methods appear in the literature. Depending on the targeted application, the proposed methods have different characteristics and are designed to perform in different setups. Thus, a method of general applicability seems to be away from the current state of the art. This chapter intends to offer an up-to-date literature review of facial feature detection algorithms. A review of the image databases and performance metrics that are used to benchmark these algorithms is also provided.

scholarly journals Salient Object Detection Techniques in Computer Vision—A Survey

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1174
Author(s):  
Ashish Kumar Gupta ◽  
Ayan Seal ◽  
Mukesh Prasad ◽  
Pritee Khanna
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Research Work ◽  
Salient Object Detection ◽  
Future Research ◽  
Automatic Identification ◽  
Salient Object ◽  
Detection Techniques

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

scholarly journals Facial Expression Recognition Using Computer Vision: A Systematic Review

2019 ◽  
Vol 9 (21) ◽  
pp. 4678 ◽  
Author(s):  
Daniel Canedo ◽  
António J. R. Neves
Keyword(s):  
Systematic Review ◽  
Computer Vision ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Local Binary Patterns ◽  
Future Research ◽  
Expression Recognition ◽  
Contemporary World

Emotion recognition has attracted major attention in numerous fields because of its relevant applications in the contemporary world: marketing, psychology, surveillance, and entertainment are some examples. It is possible to recognize an emotion through several ways; however, this paper focuses on facial expressions, presenting a systematic review on the matter. In addition, 112 papers published in ACM, IEEE, BASE and Springer between January 2006 and April 2019 regarding this topic were extensively reviewed. Their most used methods and algorithms will be firstly introduced and summarized for a better understanding, such as face detection, smoothing, Principal Component Analysis (PCA), Local Binary Patterns (LBP), Optical Flow (OF), Gabor filters, among others. This review identified a clear difficulty in translating the high facial expression recognition (FER) accuracy in controlled environments to uncontrolled and pose-variant environments. The future efforts in the FER field should be put into multimodal systems that are robust enough to face the adversities of real world scenarios. A thorough analysis on the research done on FER in Computer Vision based on the selected papers is presented. This review aims to not only become a reference for future research on emotion recognition, but also to provide an overview of the work done in this topic for potential readers.

scholarly journals Recent Advancement in Anomaly Detection in Surveillance Videos

2019 ◽  
Vol 8 (2) ◽  
pp. 964-970
Keyword(s):  
Computer Vision ◽  
Anomaly Detection ◽  
Video Data ◽  
Detection Methods ◽  
Future Research ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Computer ◽  
Banking Education ◽  
To Come

Detection of Anomaly is of a notable and emergent problem into many diverse fields like information theory, deep learning, computer vision, machine learning, and statistics that have been researched within the various application from diverse domains including agriculture, health care, banking, education, and transport anomaly detection. Newly, numbers of important anomaly detection techniques along with diverseness of sort have been watched. The main aim of this paper to come up with a broad summary of the present development on detection of an anomaly, exclusively for video data with mixed types and high dimensionalities, where identifying the anomalous behaviors and event or anomalous patterns is a significant task. The paper expresses the advantages and disadvantages of the detection methods the experiments tried on the publically available benchmark dataset to assess numerous popular and classical methods and models. The objective of this analysis is to furnish an understanding of recent computer vision and machine algorithms methods and also state-of-the-art deep learnings techniques to detect anomalies for researchers. At last, the paper delivered roughly directions for future research on an anomalies detection.

Automatic Splicing Technology in Image Splicing Processing

2014 ◽  
Vol 1014 ◽  
pp. 367-370
Author(s):  
Xiao Bo Yu ◽  
Yun Feng Zhang ◽  
Yue Gang Fu
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Computer Graphics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Research Field ◽  
Important Research ◽  
Image Splicing ◽  
Manual Intervention

Automatic splicing technology is all important research field of image processing, and has become a research focusing on the computer vision and computer graphics,and has important practical value in the fields of image splicing processing, medical image analysis and so on.On the basis of a linear transition method, this paper presents an algorithm which realizes to diminish the seams in overlap region according to the content of scenes.This algorithm avoids manual intervention during the mosaic process.With the help of automatic splicing technology based on the overlapping areas linear transition, the requirement of seamless image splicing can be met. 1.Introduction

From Face to Facial Expression

2013 ◽  
pp. 1508-1531
Author(s):  
Zakia Hammal
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Everyday Life ◽  
Facial Expression Recognition ◽  
Multidisciplinary Approach ◽  
Expression Recognition ◽  
Future Challenges ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Processing Steps

This chapter addresses recent advances in computer vision for facial expression classification. The authors present the different processing steps of the problem of automatic facial expression recognition. They describe the advances of each stage of the problem and review the future challenges towards the application of such systems to everyday life situations. The authors also introduce the importance of taking advantage of the human strategy by reviewing advances of research in psychology towards multidisciplinary approach for facial expression classification. Finally, the authors describe one contribution which aims at dealing with some of the discussed challenges.

scholarly journals Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Aqsa Rasheed ◽  
Bushra Zafar ◽  
Amina Rasheed ◽  
Nouman Ali ◽  
Muhammad Sajid ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Production Process ◽  
Digital Image Processing ◽  
Defect Detection ◽  
Digital Image ◽  
Textile Industry ◽  
Future Research ◽  
Fabric Defect Detection ◽  
Fabric Defect

There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

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