11,940 research outputs found
Recommended from our members
Efficient smile detection by Extreme Learning Machine
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
Extreme Learning Machine for land cover classification
This paper explores the potential of extreme learning machine based
supervised classification algorithm for land cover classification. In
comparison to a backpropagation neural network, which requires setting of
several user-defined parameters and may produce local minima, extreme learning
machine require setting of one parameter and produce a unique solution. ETM+
multispectral data set (England) was used to judge the suitability of extreme
learning machine for remote sensing classifications. A back propagation neural
network was used to compare its performance in term of classification accuracy
and computational cost. Results suggest that the extreme learning machine
perform equally well to back propagation neural network in term of
classification accuracy with this data set. The computational cost using
extreme learning machine is very small in comparison to back propagation neural
network.Comment: 6 pages, mapindia 2008 conferenc
Extreme Learning Machine-Based Receiver for MIMO LED Communications
This work concerns receiver design for light-emitting diode (LED) multiple
input multiple output (MIMO) communications where the LED nonlinearity can
severely degrade the performance of communications. In this paper, we propose
an extreme learning machine (ELM) based receiver to jointly handle the LED
nonlinearity and cross-LED interference, and a circulant input weight matrix is
employed, which significantly reduces the complexity of the receiver with the
fast Fourier transform (FFT). It is demonstrated that the proposed receiver can
efficiently handle the LED nonlinearity and cross-LED interference
- …