4 results
Search Results
2. Efficient smile detection by Extreme Learning Machine
- Author
-
An, L, Yang, S, and Bhanu, B
- Subjects
Facial expression analysis ,Smile detection ,Extreme Learning Machine ,Classification ,Feature extraction ,Information and Computing Sciences ,Engineering ,Psychology and Cognitive Sciences ,Artificial Intelligence & Image Processing - Abstract
Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration.
- Published
- 2015
3. Efficient smile detection by Extreme Learning Machine
- Author
-
An, Le, Yang, Songfan, and Bhanu, Bir
- Subjects
Facial expression analysis ,Smile detection ,Extreme Learning Machine ,Classification ,Feature extraction ,Information and Computing Sciences ,Engineering ,Psychology and Cognitive Sciences ,Artificial Intelligence & Image Processing - Abstract
Smile detection is a specialized task in facial expression analysis with applications such as photo selection, user experience analysis, and patient monitoring. As one of the most important and informative expressions, smile conveys the underlying emotion status such as joy, happiness, and satisfaction. In this paper, an efficient smile detection approach is proposed based on Extreme Learning Machine (ELM). The faces are first detected and a holistic flow-based face registration is applied which does not need any manual labeling or key point detection. Then ELM is used to train the classifier. The proposed smile detector is tested with different feature descriptors on publicly available databases including real-world face images. The comparisons against benchmark classifiers including Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) suggest that the proposed ELM based smile detector in general performs better and is very efficient. Compared to state-of-the-art smile detector, the proposed method achieves competitive results without preprocessing and manual registration.
- Published
- 2015
4. Connectionist Mechanisms for Cognitive Control
- Author
-
Wendelken, Carter and Shastri, Lokendra
- Subjects
spreading activation ,working memory ,decision-making - Abstract
An understanding of cognitive control is crucial for understanding high-level cognition and delineating the functional role of prefrontal cortex in supporting complex cognitive operations. In this paper, we approach the problem of cognitive control by examining the control needs of SHRUTI, a neurally plausible and cognitively motivated model of inference and decision-making. It is shown that processing via spreading activation has a number of limitations with respect to inference and decision-making, and specific forms of controlled processing is required to overcome these limitations. We propose a set of primitive, neurally plausible control mechanisms, including monitoring, filtering, selection, maintenance, organization, and manipulation, describe connectionist implementations of these primitive mechanisms, and demonstrate the use of several of these primitives in a complex control process. (c) 2004 Published by Elsevier B.V.
- Published
- 2005
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