This work presents the application of Neocognitron to the human face recognition. Using a large-scale human face database (CMU PIE), the optimal thresholds of the Neocognitron to human face recognition are verified. During the first experiment, increasing the activation thresholds of the Neocognitron, their best values to be used in the second experiment, increasing the number of training image...

Scene text recognition plays an important role in many computer vision applications. The small size of available public available scene text datasets is the main challenge when training a text recognition CNN model. In this paper, we propose a CNN based Chinese text recognition algorithm. To enlarge the dataset for training the CNN model, we design a synthetic data engine for Chinese scene char...

This paper presents an approach to estimating the parameters of continuous density HMMs for visual speech recognition. One of the key issues of image-based visual speech recognition is normalization of lip location and lighting condition prior to estimating the parameters of HMMs. We presented a normalized training method in which the normalization process is integrated in the model training. T...

Automatic cognitive state recognition is very important for military training, rehabilitation, soldier safety, and mission success. However, developing cognitive state recognition algorithms is highly challenging due to the difficulties in building a generic model whose parameters fit all subjects. Further, it is very expensive and/or time-consuming to acquire user-specific training examples th...

We investigate the utility of 3D facial landmark localisation in addressing the varying pose problem in 3D face recognition. We do not focus on the 3D landmark localisation problem itself, rather, we ask: Given the set of salient landmarks visible at some specific pose, what 3D face recognition performance can we expect, given that statistical training was performed at some other (canonical) po...

In this study we introduce and demystify a novel signal pattern recognition method, Spectral Collaborative Representation based Classification (SCRC) and demonstrate its application for recognition of hand gestures and postures using Electromyography sensors. A recently released Thalmic Labs MYO armband is used to gather muscle electromyography signals. Along with the new signal pattern classif...

in this research, an iterative approach is employed to recognize and classify control chart patterns. to do this, by taking new observations on the quality characteristic under consideration, the maximum likelihood estimator of pattern parameters is first obtained and then the probability of each pattern is determined. then using bayes’ rule, probabilities are updated recursively. finally, when...

In this paper, a new formulation for discriminative training of HMMs is presented. This formulation uses a properly trained MLP in a simple interconnection with HMMs called “Cascade HMM/ANN Hybrid”. Our training algorithm has simple realization in comparison with other discriminative training for HMMs such as MDI and MMI. We also present a rigid mathematical proof of its convergence. We found t...

In this paper, we present a study on sample preselection in large training data set for CNN-based classification. To do so, we structure the input data set in a network representation, namely the Relative Neighbourhood Graph, and then extract some vectors of interest. The proposed preselection method is evaluated in the context of handwritten character recognition, by using two data sets, up to...

Sign language recognition (SLR) can provide a helpful tool for the communication between the deaf and the external world. This paper proposed a component-based vocabulary extensible SLR framework using data from surface electromyographic (sEMG) sensors, accelerometers (ACC), and gyroscopes (GYRO). In this framework, a sign word was considered to be a combination of five common sign components, ...