Hidden Markov model for human to computer interaction: A study on human hand gesture recognition

• Main Authors: Bilal, Sara Mohammed Osman Saleh, Akmeliawati, Rini, Shafie, Amir Akramin, Salami, Momoh Jimoh Emiyoka
• Format: Article
• Language: English
• Published: SpringerLink 2013
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://irep.iium.edu.my/16246/1/10.1007_s10462-011-9292-0.pdf; http://irep.iium.edu.my/16246/; http://dx.doi.org/10.1007/s10462-011-9292-0

Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.