A Comprehensive Video Dataset for Multi-Modal Recognition Systems

• Main Authors: Anand Handa, Rashi Agarwal, Narendra Kohli
• Format: Article
• Language: English
• Published: Ubiquity Press 2019-11-01
• Series: Data Science Journal
• Subjects: machine leaning; deep learning; video datasets; convolutional neural network
• Online Access: https://datascience.codata.org/articles/909

This paper presents a comprehensive, highly defined and fully labelled video dataset. This dataset consists of videos related to 67 different subjects. The videos contain similar text and the text contains digits from 1 to 20 recited by 67 different subjects using the same experimental setup. This dataset can be used as a unique resource for researchers and analysts for training deep neural networks to build highly efficient and accurate recognition models in various domains of computer vision such as face recognition model, expression recognition model, speech recognition model, text recognition, etc. In this paper, we also train models related to face recognition and speech recognition on our dataset and also compare the results with the publically available datasets to show the effectiveness of our dataset. The experimental results show that our comprehensive dataset is more accurate than other dataset on which the models are tested.