Chapter 5 Application of machine learning algorithms for facial expression analysis
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Uma V. Maheswari
Abstract
Nowadays, facial expression analysis (FEA) is becoming an important application on various fields such as medicine, education, entertainment and crime analysis because it helps to analyze where no verbal communication is possible. FEA is being done after face recognition and depends on the feature extraction of how efficiently it is generated. Therefore, classification plays a vital role to acquire the necessary output to analyze the correct expression. In addition, machine learning (ML) and deep learning algorithms are useful to classify the data as system requires either structured-like text or unstructured-like images and videos perhaps to analyze the expression, and image input is preferred by the system as well because the face image consists of a kind of information like texture of organized features, age, gender and shape which cannot be described properly by the textual annotation to a corresponding image. The system can be done in different ways: either it can apply the deep learning algorithms on raw data, or can apply ML algorithms on the preprocessed images based on the user requirement. This chapter discusses the challenges and potential ML algorithms and efficient deep learning algorithms to recognize the automatic expression of humans to prop up with the significant areas such as human computer interaction, psychology in medical field, especially to analyze the behavior of suspected people in crowded areas probably in airports and so on. In recent years, ML algorithms had become very popular in the field of data retrieval to improve its efficiency and accuracy. A new state-ofthe- art image retrieval called ML algorithms plays an imperative role to decrease the gap semantically between the user expectation and images available in the database. This chapter presents a comprehensive study of ML algorithms such as supervised, unsupervised and a sequence of both. Furthermore, the demonstration of various ML algorithms is used for image classification, and of clustering which also represents the summary and comparison of ML algorithms for various datasets like COREL and face image database. Finally, the chapter concludes with the challenges and few recommendations of ML algorithms in image retrieval.
Abstract
Nowadays, facial expression analysis (FEA) is becoming an important application on various fields such as medicine, education, entertainment and crime analysis because it helps to analyze where no verbal communication is possible. FEA is being done after face recognition and depends on the feature extraction of how efficiently it is generated. Therefore, classification plays a vital role to acquire the necessary output to analyze the correct expression. In addition, machine learning (ML) and deep learning algorithms are useful to classify the data as system requires either structured-like text or unstructured-like images and videos perhaps to analyze the expression, and image input is preferred by the system as well because the face image consists of a kind of information like texture of organized features, age, gender and shape which cannot be described properly by the textual annotation to a corresponding image. The system can be done in different ways: either it can apply the deep learning algorithms on raw data, or can apply ML algorithms on the preprocessed images based on the user requirement. This chapter discusses the challenges and potential ML algorithms and efficient deep learning algorithms to recognize the automatic expression of humans to prop up with the significant areas such as human computer interaction, psychology in medical field, especially to analyze the behavior of suspected people in crowded areas probably in airports and so on. In recent years, ML algorithms had become very popular in the field of data retrieval to improve its efficiency and accuracy. A new state-ofthe- art image retrieval called ML algorithms plays an imperative role to decrease the gap semantically between the user expectation and images available in the database. This chapter presents a comprehensive study of ML algorithms such as supervised, unsupervised and a sequence of both. Furthermore, the demonstration of various ML algorithms is used for image classification, and of clustering which also represents the summary and comparison of ML algorithms for various datasets like COREL and face image database. Finally, the chapter concludes with the challenges and few recommendations of ML algorithms in image retrieval.
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- About editors IX
- List of contributors XI
- Chapter 1. A framework for applying artificial intelligence (AI) with Internet of nanothings (IoNT) 1
- Chapter 2 Opportunities and challenges in transforming higher education through machine learning 17
- Chapter 3 Efficient renewable energy integration: a pertinent problem and advanced time series data analytics solution 31
- Chapter 4 A comprehensive review on the application of machine learning techniques for analyzing the smart meter data 53
- Chapter 5 Application of machine learning algorithms for facial expression analysis 77
- Chapter 6 Prediction of quality analysis for crop based on machine learning model 97
- Chapter 7 Data model recommendations for real-time machine learning applications: a suggestive approach 115
- Chapter 8 Machine learning for sustainable agriculture 129
- Chapter 9 Application of machine learning in SLAM algorithms 147
- Chapter 10 Machine learning for weather forecasting 161
- Chapter 11 Applications of conventional machine learning and deep learning for automation of diagnosis: case study 175
- Index 199
Chapters in this book
- Frontmatter I
- Preface V
- Contents VII
- About editors IX
- List of contributors XI
- Chapter 1. A framework for applying artificial intelligence (AI) with Internet of nanothings (IoNT) 1
- Chapter 2 Opportunities and challenges in transforming higher education through machine learning 17
- Chapter 3 Efficient renewable energy integration: a pertinent problem and advanced time series data analytics solution 31
- Chapter 4 A comprehensive review on the application of machine learning techniques for analyzing the smart meter data 53
- Chapter 5 Application of machine learning algorithms for facial expression analysis 77
- Chapter 6 Prediction of quality analysis for crop based on machine learning model 97
- Chapter 7 Data model recommendations for real-time machine learning applications: a suggestive approach 115
- Chapter 8 Machine learning for sustainable agriculture 129
- Chapter 9 Application of machine learning in SLAM algorithms 147
- Chapter 10 Machine learning for weather forecasting 161
- Chapter 11 Applications of conventional machine learning and deep learning for automation of diagnosis: case study 175
- Index 199
