Machine learning for face, emotion, and pain recognition:
This Spotlight explains how to build an automated system for face, emotion, and pain recognition. These steps include pre-processing, face detection and segmentation, feature extraction, and finally and most importantly, recognition to classify features and show the accuracy of the system. State-of-...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bellingham, Washington, USA
SPIE Press
[2018]
|
| Schriftenreihe: | SPIE spotlight series
vol. SL37 |
| Schlagworte: | |
| Online-Zugang: | FHD01 UBG01 Volltext |
| Zusammenfassung: | This Spotlight explains how to build an automated system for face, emotion, and pain recognition. These steps include pre-processing, face detection and segmentation, feature extraction, and finally and most importantly, recognition to classify features and show the accuracy of the system. State-of-the-art algorithms are used to describe all possible solutions of each step. Pre-processing involves algorithms to reduce noise and improve the illumination of images. For face detection and segmentation, several approaches are described to detect a face in images: Viola-Jones, color-based approaches, histogram-based approaches, and morphological operation. Local binary patterns, edge detectors, wavelets, discrete Cosine transformation, Gabor filters, and fuzzified features are used for feature extraction. The last step includes three approaches for recognition: classification techniques (with a special focus on deep learning), statistical modeling, and distance/similarity measures |
| Beschreibung: | 1 Online-Ressource (vi, 101 Seiten) |
| ISBN: | 9781510619869 |
| DOI: | 10.1117/3.2322572 |
Internformat
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| 505 | 8 | |a 1. Introduction -- 2. Face recognition: 2.1. Introduction; 2.3. PDF-based face recognition; 2.4. Data fusion -- 3. Emotion recognition: 3.1. Speech-based emotion recognition; 3.2. Other modalities; 3.3. Gesture-based emotion recognition; 3.4. Face-expression recognition -- 4. Pain recognition -- 5. Face databases: 5.1. Elicitation methods; 5.2. Categories of emotion; 5.3. Database types; 5.4. Pain databases -- Acknowledgments -- References | |
| 520 | |a This Spotlight explains how to build an automated system for face, emotion, and pain recognition. These steps include pre-processing, face detection and segmentation, feature extraction, and finally and most importantly, recognition to classify features and show the accuracy of the system. State-of-the-art algorithms are used to describe all possible solutions of each step. Pre-processing involves algorithms to reduce noise and improve the illumination of images. For face detection and segmentation, several approaches are described to detect a face in images: Viola-Jones, color-based approaches, histogram-based approaches, and morphological operation. Local binary patterns, edge detectors, wavelets, discrete Cosine transformation, Gabor filters, and fuzzified features are used for feature extraction. The last step includes three approaches for recognition: classification techniques (with a special focus on deep learning), statistical modeling, and distance/similarity measures | ||
| 650 | 4 | |a Human face recognition (Computer science) | |
| 650 | 4 | |a Emotion recognition | |
| 700 | 1 | |a Gorbova, Jelena |e Verfasser |4 aut | |
| 700 | 1 | |a Hammer, Rain Eric |e Verfasser |4 aut | |
| 700 | 1 | |a Rasti, Pejman |e Verfasser |4 aut | |
| 700 | 1 | |a Noroozi, Fatemeh |d ca. 20./21. Jh. |e Verfasser |0 (DE-588)1153913615 |4 aut | |
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Datensatz im Suchindex
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| author | Anbarjafari, Gholamreza ca. 20./21. Jh Gorbova, Jelena Hammer, Rain Eric Rasti, Pejman Noroozi, Fatemeh ca. 20./21. Jh |
| author_GND | (DE-588)1153915103 (DE-588)1153913615 |
| author_facet | Anbarjafari, Gholamreza ca. 20./21. Jh Gorbova, Jelena Hammer, Rain Eric Rasti, Pejman Noroozi, Fatemeh ca. 20./21. Jh |
| author_role | aut aut aut aut aut |
| author_sort | Anbarjafari, Gholamreza ca. 20./21. Jh |
| author_variant | g a ga j g jg r e h re reh p r pr f n fn |
| building | Verbundindex |
| bvnumber | BV045058936 |
| collection | ZDB-50-SPI ZDB-30-PQE |
| contents | 1. Introduction -- 2. Face recognition: 2.1. Introduction; 2.3. PDF-based face recognition; 2.4. Data fusion -- 3. Emotion recognition: 3.1. Speech-based emotion recognition; 3.2. Other modalities; 3.3. Gesture-based emotion recognition; 3.4. Face-expression recognition -- 4. Pain recognition -- 5. Face databases: 5.1. Elicitation methods; 5.2. Categories of emotion; 5.3. Database types; 5.4. Pain databases -- Acknowledgments -- References |
| ctrlnum | (OCoLC)1042914382 (DE-599)BVBBV045058936 |
| doi_str_mv | 10.1117/3.2322572 |
| format | Electronic eBook |
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| id | DE-604.BV045058936 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:07:27Z |
| institution | BVB |
| isbn | 9781510619869 |
| language | English |
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| physical | 1 Online-Ressource (vi, 101 Seiten) |
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| spelling | Anbarjafari, Gholamreza ca. 20./21. Jh. Verfasser (DE-588)1153915103 aut Machine learning for face, emotion, and pain recognition by Gholamreza Anbarjafari, Jelena Gorbova, Rain Eric Hammer, Pejman Rasti, and Fatemeh Noroozi Bellingham, Washington, USA SPIE Press [2018] 1 Online-Ressource (vi, 101 Seiten) txt rdacontent sti rdacontent c rdamedia cr rdacarrier SPIE spotlight series vol. SL37 1. Introduction -- 2. Face recognition: 2.1. Introduction; 2.3. PDF-based face recognition; 2.4. Data fusion -- 3. Emotion recognition: 3.1. Speech-based emotion recognition; 3.2. Other modalities; 3.3. Gesture-based emotion recognition; 3.4. Face-expression recognition -- 4. Pain recognition -- 5. Face databases: 5.1. Elicitation methods; 5.2. Categories of emotion; 5.3. Database types; 5.4. Pain databases -- Acknowledgments -- References This Spotlight explains how to build an automated system for face, emotion, and pain recognition. These steps include pre-processing, face detection and segmentation, feature extraction, and finally and most importantly, recognition to classify features and show the accuracy of the system. State-of-the-art algorithms are used to describe all possible solutions of each step. Pre-processing involves algorithms to reduce noise and improve the illumination of images. For face detection and segmentation, several approaches are described to detect a face in images: Viola-Jones, color-based approaches, histogram-based approaches, and morphological operation. Local binary patterns, edge detectors, wavelets, discrete Cosine transformation, Gabor filters, and fuzzified features are used for feature extraction. The last step includes three approaches for recognition: classification techniques (with a special focus on deep learning), statistical modeling, and distance/similarity measures Human face recognition (Computer science) Emotion recognition Gorbova, Jelena Verfasser aut Hammer, Rain Eric Verfasser aut Rasti, Pejman Verfasser aut Noroozi, Fatemeh ca. 20./21. Jh. Verfasser (DE-588)1153913615 aut Erscheint auch als Online-Ausgabe, epub 978-1-5106-1987-6 Erscheint auch als Online-Ausgabe, mobi 978-1-5106-1988-3 https://doi.org/10.1117/3.2322572 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Anbarjafari, Gholamreza ca. 20./21. Jh Gorbova, Jelena Hammer, Rain Eric Rasti, Pejman Noroozi, Fatemeh ca. 20./21. Jh Machine learning for face, emotion, and pain recognition 1. Introduction -- 2. Face recognition: 2.1. Introduction; 2.3. PDF-based face recognition; 2.4. Data fusion -- 3. Emotion recognition: 3.1. Speech-based emotion recognition; 3.2. Other modalities; 3.3. Gesture-based emotion recognition; 3.4. Face-expression recognition -- 4. Pain recognition -- 5. Face databases: 5.1. Elicitation methods; 5.2. Categories of emotion; 5.3. Database types; 5.4. Pain databases -- Acknowledgments -- References Human face recognition (Computer science) Emotion recognition |
| title | Machine learning for face, emotion, and pain recognition |
| title_auth | Machine learning for face, emotion, and pain recognition |
| title_exact_search | Machine learning for face, emotion, and pain recognition |
| title_full | Machine learning for face, emotion, and pain recognition by Gholamreza Anbarjafari, Jelena Gorbova, Rain Eric Hammer, Pejman Rasti, and Fatemeh Noroozi |
| title_fullStr | Machine learning for face, emotion, and pain recognition by Gholamreza Anbarjafari, Jelena Gorbova, Rain Eric Hammer, Pejman Rasti, and Fatemeh Noroozi |
| title_full_unstemmed | Machine learning for face, emotion, and pain recognition by Gholamreza Anbarjafari, Jelena Gorbova, Rain Eric Hammer, Pejman Rasti, and Fatemeh Noroozi |
| title_short | Machine learning for face, emotion, and pain recognition |
| title_sort | machine learning for face emotion and pain recognition |
| topic | Human face recognition (Computer science) Emotion recognition |
| topic_facet | Human face recognition (Computer science) Emotion recognition |
| url | https://doi.org/10.1117/3.2322572 |
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