Detection and classification of hand gestures based on the sign language using handcrafted and deep learning methods
Hand gesture datasetThe present work proposes recognizing static hand gestures taken under invariations features like scale, rotation, translation, illumination, noise, and background. We use the alphabet of the sign language of Peru (LSP). For that, digital image processing techniques are used to eliminate or reduce noise, improve the contrast under a variant illumination, separate the hand from the background of the image, and finally detect and cut the region containing the hand gesture. We develop two convolutional neural networks (CNN) to classify the 24 hand gestures. These two CNNs were developed with different amounts of layers and parameters per layer. The tests showed that the first and second CNN presents an accuracy of 95.37% and 96.20% respectively, using the database of 24 static hand gestures developed.
Jose Luis Flores Campana
Ph.D. in Computer Science
Jose Luis Flores received his B.Sc. in Computer and Software Engineering from the University of San Antonio Abad de Cusco (UNSAAC), Peru, in 2016. As a bachelor’s student, Jose worked on a research paper related to the recognition and classification of hand gestures based on sign language using machine and deep learning techniques. After, Jose obtained his M.Sc in Computer Science from the University of Campinas (Unicamp), Brazil, in 2020. As a master’s student, Jose was part of a team of researchers from SAMSUNG Brasil and UNICAMP. In this team he worked on two projects, “Multilingual text detection and recognition in images and videos” and “Generation of parallax motion effects”. In 2024, Jose received his Ph.D. degree from the University of Campinas (Unicamp), Brazil. As a Ph.D. student, Jose worked on topics such as Image Inpainting and Image Synthesis, focusing his research on Deep Learning models such as Generative Adversarial Networks and Vision Transformer. His research focuses on Machine Learning, Deep Learning, and Image Processing, with specialization in Text Detection and Recognition in images and videos, Image Inpainting, and Image Synthesis. He currently works as a software engineer at Loggi. He currently works as a software engineer at Loggi. Here, Jose participated in many projects related to back-end, front-end and ML. For back-end and ML, Jose worked with the development of APIs, services and cloud computing as AWS services (e.g. AWS S3). As front-end, Jose worked with React, javascript, NodeJs, HTML, and Typescript.