• Python 3.x
• PyTorch 0.4.0

Our project focuses on gesture recognition, a field within computer vision and human-computer interaction that aims to interpret human gestures via Deep Learning. For our study, we have chosen the Jester dataset as it provides a compre- hensive collection of hand gesture videos. The Jester dataset is particularly well-suited for our purposes due to the following reasons: • Large-scale and Diverse: The Jester dataset contains a large number of videos featuring diverse hand gestures, providing a rich source of data for training and evaluation of gesture recognition algorithms. • Real-world Gestures: The gestures captured in the Jester dataset rep- resent common human actions and interactions, making it applicable to a wide range of practical applications. • Annotated Data: The dataset comes with pre-defined labels for each gesture, that facilitates supervised learning and evaluation of gesture recog- nition models. • Community Benchmark: The availability of the Jester dataset has led to it being widely used as a benchmark for evaluating gesture recognition algorithms, allowing for comparison and advancement of research in the field. By leveraging the Jester dataset, our project aims to develop and evaluate state-of-the-art gesture recognition models that can accurately interpret and classify human gestures in real-time.

pip install -r requirements.txt