# Visual Static Hand Gesture Recognition Using Convolutional Neural Network
## 1 Introduction
[18], [PERSON] and [PERSON] converted spatial domain-format hand gesture images into multi-class domain-format images using the Gabor filtering approach. They employed both Bayesian and Naive Bayes classifiers to categorize the test hand gesture images into different classes. The researchers found that the naive Bayes classifier produced higher levels of classification accuracy than the Bayesian classification methodology, owing to its straightforward architecture pattern. The data is acquired by a 7-megapixel camera. The dataset used has a total of ten hand gestures. For each hand gesture, there are 18 images, of which 5 are used for training and 13 for final testing. The model has an accuracy of 90%. In [19], [PERSON] and [PERSON] used an _SVM_ classification approach in 2013 to categorize hand gesture images into various classes. The training set for the detection phase consisted of over 800 positive samples and 1500 negative image samples. The researchers achieved a sensitivity of 89.6%, a specificity of 79.9%, and an accuracy of 85.7%. However, the error rate was high in this method, and it was not suitable for fast-moving background and foreground object images. In ref. [20; 21], Authors utilized naive Bayes Classifier and _SVM_ approaches for recognizing gestures, but these methods were unable to handle large training datasets and needed a large number of training samples. To overcome these limitations, the current study introduces a _CNN_ classifier, which does not require a large number of training samples and has a low complexity level. In ref. [22], [PERSON] et al. developed a hand gesture recognition system using a hidden Markov model. The authors constructed a Markov model for the foreground fingers in a hand gesture image. This model was used in both the training and testing of the binary classification approach, giving it an accuracy of 90.1%. In ref. [23], the hand postures are classified using the shape, texture, and color features with an _SVM_ classifier. The proposed system utilizes a Bayesian model of visual attention to generate a saliency map and to detect and identify the hand region, and they reported an accuracy of 94.36% on the NUS II dataset that we use in our research. In ref. [24], the authors proposed a _CNN_ model with two convolutional layers, two max-pooling layers, and one last fully connected layer. Dropout and activation functions are optional. However, they reported better results using both of them. The best accuracy reported on the NUS-II dataset was 89.1% by adding the dropout and activation functions. In our research paper,we will compare our results with the ones in refs. [23; 24] as they reported results on the same challenging dataset we use and they also use _CNN_. In this research, we propose a _CNN_ to recognize static hand gestures against complex backgrounds. The objective is to increase the accuracy of correctly identified gestures. Testing the accuracy of the model is achieved by comparing the true label of the image with the predicted label. The efficiency of deep learning in extracting and classifying high-level aspects of data has recently been the focus of existing research. In summary, the contributions of this paper are adding the power of newly proposed preprocessing techniques in this research for the images using skin segmentation and data augmentation with the power of using _CNN_ for classifying images. To the best of our knowledge, there has been no previous publication that has performed skin segmentation on the NUS II dataset using the same methodology we used. In addition, there is no paper that reported any results of combining both the _CNN_ model and skin segmentation on a complex dataset such as the NUS II dataset, as we performed. We compare our accuracy on the NUS II dataset to the one in refs. [23; 24], as they both use different state-of-the-art methods on the same dataset. The accuracy of our proposed method has improved from the one in ref.