Human face and hand detection, recognition and tracking are important research areas for many computer interaction applications. Face and hand are considered as human skin blobs, which fall in a compact region of colour spaces. Limitations arise from the fact that human skin has common properties and can be defined in various colour spaces after applying colour normalization. The model therefore, has to accept a wide range of colours, making it more susceptible to noise. We have addressed this problem and propose that the skin colour could be defined separately for every person. This is expected to reduce the errors. To detect human skin colour pixels and to decrease the number of false alarms, a prior face or hand detection model has been developed using Haar-like and AdaBoost technique. To decrease the cost of computational time, a fast search algorithm for skin detection is proposed. The level of performance reached in terms of detection accuracy and processing time allows this approach to be an adequate choice for real-time skin blob tracking.

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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.

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Malaysian Sign Language (MSL) is the main language that is commonly used by the hearing and speech impaired person in Malaysian. The SL (SL) involves hand movement, and hand gestures. In order to help people who are not familiar, but need to understand a particular SL, an automatic SL recognition system is highly required. The research in this area, especially for MSL, has been conducted by many researchers, but one of the main challenges in this research is the availability of suitable sign database for the recognition. The existing databases, especially which of MSL database, are provided often without a proper standard of image resolution, structure and compression that are sufficiently good for research purpose. To provide comprehensive information for the research on MSL, the MSL database is highly required. In this project, a MSL database is developed. The database is the first of the kind and developed for research purpose. In general, the structure of the MSL database is classified into groups that deal with the hand movement, hand gestures, and hand location. For the classification in our proposed database, the MSL is classified into One Hand, Two Hands, Static, and Dynamic. This classification is made to ease researchers in defining the research method for each type offhand signing.