67 research outputs found
New Face Representation Using Compressive Sensing
No full textIn this paper we present a new descriptor for
representing face images. We used compressive sensing
concept to prepare a Gaussian Random or Binary
Random Measurement Matrix (GRMM). We simply
project the face images to new space using GRMM.
Classification is then performed using nearest neighbor
classifiers. System performance is very promising and
comparable with the well-known algorithms in the
literature
Comparative Study on Facial Expression Recognition using Gabor and Dual-Tree Complex Wavelet Transforms
No full text
Face recognition using ensemble statistical local descriptors
No full textThe use of data fusion can be of a enormous help in boosting classification performance. Feature fusion is a data fusion technique that is being considered in this study. The effect of fusing different feature descriptors extracted by using histogram-based local feature extraction algorithms on the performance of the face recognition problem is investigated. Feature fusion/concatenation of more than one generated feature descriptor is applied. The impact of fused two and three feature descriptors on the system performance is evaluated when the training set is limited to only one-shot per person. Extensive experiments are carried out using two well-known face databases. Comparisons are conducted among different algorithms for extraction of the local statistical feature descriptors of the face images. The obtained results show that feature fusion of the descriptors can significantly improve the performance with certain feature descriptors
Spectrogram-Based Arrhythmia Classification Using Three-Channel Deep Learning Model with Feature Fusion
No full textThis paper introduces a novel deep learning model for ECG signal classification using feature fusion. The proposed methodology transforms the ECG time series into a spectrogram image using a short-time Fourier transform (STFT). This spectrogram is further processed to generate a histogram of oriented gradients (HOG) and local binary pattern (LBP) features. Three separate 2D convolutional neural networks (CNNs) then analyze these three image representations in parallel. To enhance performance, the extracted features are concatenated before feeding them into a gated recurrent unit (GRU) model. The proposed approach is extensively evaluated on two ECG datasets (MIT-BIH + BIDMC and MIT-BIH) with three and five classes, respectively. The experimental results demonstrate that the proposed approach achieves superior classification accuracy compared to existing algorithms in the literature. This suggests that the model has the potential to be a valuable tool for accurate ECG signal classification, aiding in the diagnosis and treatment of various cardiovascular disorders
A MONOGENIC LOCAL GABOR BINARY PATTERN FOR FACIAL EXPRESSION RECOGNITION
No full textThe paper implements a monogenic-Local Binary Pattern (mono-LBP) algorithm on a local Gabor Pattern (LGP). The proposed algorithm is applied at different scales of the Gabor kernel with different normalization schemes. Results from the two best performing normalization algorithms with mono-LBP are fused at score level to obtain an improved performance. Moreover, performance comparison is done with other variants of LGP algorithm and also the effects of various normalization techniques are investigated. Experimental results on JAFFE facial expression database show that the new technique has the best average performance compared to its counterparts using distance metrics as a classifier.
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