Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech

Recent advancements in the field of computer vision with the help of deep neural networks have led us to explore and develop many existing challenges that were once unattended due to the lack of necessary technologies. Hand Sign/Gesture Recognition is one of the significant areas where the deep neural network is making a substantial impact. In the last few years, a large number of researches has been conducted to recognize hand signs and hand gestures, which we aim to extend to our mother-tongue, Bangla (also known as Bengali). The primary goal of our work is to make an automated tool to aid the people who are unable to speak. We developed a system that automatically detects hand sign based digits and speaks out the result in Bangla language. According to the report of the World Health Organization (WHO), 15% of people in the world live with some kind of disabilities. Among them, individuals with communication impairment such as speech disabilities experience substantial barrier in social interaction. The proposed system can be invaluable to mitigate such a barrier. The core of the system is built with a deep learning model which is based on convolutional neural networks (CNN). The model classifies hand sign based digits with 92% accuracy over validation data which ensures it a highly trustworthy system. Upon classification of the digits, the resulting output is fed to the text to speech engine and the translator unit eventually which generates audio output in Bangla language. A web application to demonstrate our tool is available at http://bit.ly/signdigits2banglaspeech

Residual Graph Convolutional Neural Network for Gait Recognition across Various Walking Conditions

Over the years, extensive attention was given to a person identification task to prevent fraudulent activities. Several techniques have been developed for verifying persons from video or image by considering biometrics such as face, palm print, iris, and gait. Among all the traits mentioned above, gait is an unobtrusive and easily collectible biometric that can be observed without hindrance to the subject's activity. However, gait recognition performance can deteriorate under challenging conditions, including unconstrained path, bulky clothing, and different viewing angles. To provide an effective solution to gait recognition under these conditions, this thesis pioneers developing the Residual Connection-based Graph Convolutional Neural Network architecture for robust and reliable gait recognition. The proposed methodology incorporates residual connections for gait recognition from videos. Furthermore, the proposed system is lightweight in terms of computational cost, making the model deployable in practice. CASIA-B and AVA multi-view Gait datasets are used to evaluate the efficacy of the proposed method. The developed system attained 97.03% mean accuracy under normal walking conditions, 90.77% mean accuracy under bag carrying conditions, 89.90% mean accuracy under bulky clothes wearing conditions on CASIA-B gait dataset, and 98.85% mean accuracy for unconstrained gaits on AVA multi-view Gait dataset. The findings demonstrate that the proposed methodology outperformed other state-of-the-art gait recognition systems under challenging walking conditions

Biometric Systems De-Identification: Current Advancements and Future Directions

Biometric de-identification is an emerging topic of research within the information security domain that integrates privacy considerations with biometric system development. A comprehensive overview of research in the context of authentication applications spanning physiological, behavioral, and social-behavioral biometric systems and their privacy considerations is discussed. Three categories of biometric de-identification are introduced, namely complete de-identification, auxiliary biometric preserving de-identification, and traditional biometric preserving de-identification. An overview of biometric de-identification in emerging domains such as sensor-based biometrics, social behavioral biometrics, psychological user profile identification, and aesthetic-based biometrics is presented. The article concludes with open questions and provides a rich avenue for subsequent explorations of biometric de-identification in the context of information privacy

BanglaLekha-Isolated: A multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters

BanglaLekha-Isolated, a Bangla handwritten isolated character dataset is presented in this article. This dataset contains 84 different characters comprising of 50 Bangla basic characters, 10 Bangla numerals and 24 selected compound characters. 2000 handwriting samples for each of the 84 characters were collected, digitized and pre-processed. After discarding mistakes and scribbles, 1,66,105 handwritten character images were included in the final dataset. The dataset also includes labels indicating the age and the gender of the subjects from whom the samples were collected. This dataset could be used not only for optical handwriting recognition research but also to explore the influence of gender and age on handwriting. The dataset is publicly available at https://data.mendeley.com/datasets/hf6sf8zrkc/2