Machine learning for face, emotion, and pain recognition / by Gholamreza Anbarjafari, Jelena Gorbova, Rain Eric Hammer, Pejman Rasti, and Fatemeh Noroozi
- Anbarjafari, Gholamreza
- Bellingham, Washington (1000 20th St. Bellingham WA 98225-6705 USA) : SPIE, 2018.
- Physical Description:
- 1 online resource (106 pages) : illustrations
- Additional Creators:
- Gorbova, Jelena, Hammer, Rain Eric, Rasti, Pejman, Noroozi, Fatemeh, and Society of Photo-optical Instrumentation Engineers
- Restrictions on Access:
- Restricted to subscribers or individual electronic text purchasers.
- 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.
- 9781510619869 pdf
- "SPIE Digital Library."--Website.
- Bibliography Note:
- Includes bibliographical references (page 81-106).
- Technical Details:
- Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
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