Deep learning based static hand gesture recognition

Abstract

Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV and Intel Real Sense libraries are used. The subsystem for features extracting and gestures classification is based on the modified VGG-16 by using the TensorFlow&Keras deep learning framework. Performance of the static gestures recognition system is evaluated using maching learning metrics. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy both at the training and testing stages.

An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning

“…The accuracy, ROC-AUC, precision, recall, and F1-Score measures are used as metrics for evaluation.  Accuracy: the overall effectiveness, precision, and efficacy of the classification model can be described as accuracy [29], [30].  ROC-AUC: ROC is a probability curve, and AUC represents the degree or measure of separability.…”

“… ROC-AUC: ROC is a probability curve, and AUC represents the degree or measure of separability. They indicate how well a model can differentiate amongst classes [30].  Precision: this is the ratio of predicted positives, which are accurately positive [30].…”

“…They indicate how well a model can differentiate amongst classes [30].  Precision: this is the ratio of predicted positives, which are accurately positive [30].  Recall: also known as "sensitivity or true positive rate," speak of the amount of positively predicted actual positive occurrences [29], [30].…”

“… Precision: this is the ratio of predicted positives, which are accurately positive [30].  Recall: also known as "sensitivity or true positive rate," speak of the amount of positively predicted actual positive occurrences [29], [30].  F-Measure: the "F-measure" is recognized as the unifier means of precision and recall [29], [30].…”

Convolutional neural networks framework for human hand gesture recognition

“…Hand gesture recognition approaches provide an inspirational area of researches since they are capable of facilitating communications and offering a natural interaction means to be utilized in a diversity of applications. These approaches can be categorized into sensor-based and vision-based approaches [8]- [10]. In sensor-based approaches, wearable sensors are attached directly to the hand on gloves for detecting the physical reaction of finger bending or hand movements.…”