Face Expression Recognition Using a Combination of Local Binary Patterns and Local Phase Quantization

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

Download Full-text

An Integrated 3D Face-Expression Recognition Approach

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings ◽

10.1109/icassp.2006.1660858 ◽

2006 ◽

Cited By ~ 2

Author(s):

Chao Li ◽

A. Barreto

Keyword(s):

Expression Recognition ◽

3D Face ◽

Face Expression ◽

Face Expression Recognition

Download Full-text

Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

BioMed Research International ◽

10.1155/2016/4783801 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 13

Author(s):

Ji-Yong An ◽

Fan-Rong Meng ◽

Zhu-Hong You ◽

Yu-Hong Fang ◽

Yu-Jun Zhao ◽

...

Keyword(s):

Protein Sequences ◽

Relevance Vector Machine ◽

Experimental Results ◽

Computational Method ◽

Support Vector ◽

Svm Classifier ◽

Local Phase ◽

Local Phase Quantization ◽

Phase Quantization ◽

Better Than

We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments onYeastandHumandatasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on theYeastdataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

Download Full-text