Precise iris tracking is an important tool in assistive technology, and has many advanced applications such as in human-computer interactions and driver fatigue detection. Features such as shape, colour, and size of the iris enable specific position and centre of the iris to be tracked during its movement. The iris tracking system is divided into four stages: image acquisition, face detection, eye detection, and eye tracking. This study proposes a new method for iris tracking using a generalized particle filter. This approach utilizes a sample set of the tracked iris which is created at the beginning of the tracking process. The prior representation and position of the tracked iris are then predicted depending on the minimization of parameters of the proposed generalized probabilistic distribution. Results of the experiments show that the proposed method has high accuracy and can be used to efficiently track the at a shorter length of time.

Iris tracking; Particle filter; β-Distribution; Biometrics; Fatigue detection

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