Learn More
This study is focused on improving the recognition rate and processing time of facial recognition systems. First, the skin is detected by pixel based methods to reduce the searching space for maximum rejection classifier (MRC) which detects the face. The detected face is normalized by a discrete cosine transform (DCT) and down-sampled by Bessel transform.(More)
Today Human Computer Interaction (HCI) is one of the most important topics in machine vision and image processing fields. The ability to handle multi-pose facial expressions is important for computers to understand affective behavior under less constrained environment. In this paper, we propose a SURF (Speeded-Up Robust Features) boosting framework to(More)
—As an essential way of human emotional behavior understanding, speech emotion recognition (SER) has attracted a great deal of attention in human-centered signal processing. Accuracy in SER heavily depends on finding good affect-related, discriminative features. In this paper, we propose to learn affect salient features for SER using convolutional neural(More)
The key security challenges and solutions on the cloud have been investigated in this paper with the help of literature reviews and an experimental model created on OPNET that is simulated to produce useful statistics to establish the approach that the cloud computing service providers should take to provide optimal security and compliance. The literatures(More)
Deep learning systems, such as Convolutional Neural Networks (CNNs), can infer a hierarchical representation of input data that facilitates categorization. In this paper, we propose to learn affect-salient features for Speech Emotion Recognition (SER) using semi-CNN. The training of semi-CNN has two stages. In the first stage, unlabeled samples are used to(More)
A neural network (NN) pruning method optimized with particle swarm optimization (PSO) algorithm is proposed in this paper. Correlation merging algorithm is an important pruning method in NN structure design. Unlike general training method with back-propagation (BP), this paper uses PSO algorithm in the pruning process. The PSO is used to optimize the(More)