  With the advancement of IoT and artificial intelligence technologies, and the
need for rapid application growth in fields such as security entrance control
and financial business trade, facial information processing has become an
important means for achieving identity authentication and information security.
In this paper, we propose a multi-feature fusion algorithm based on integral
histograms and a real-time update tracking particle filtering module. First,
edge and colour features are extracted, weighting methods are used to weight
the colour histogram and edge features to describe facial features, and fusion
of colour and edge features is made adaptive by using fusion coefficients to
improve face tracking reliability. Then, the integral histogram is integrated
into the particle filtering algorithm to simplify the calculation steps of
complex particles. Finally, the tracking window size is adjusted in real time
according to the change in the average distance from the particle centre to the
edge of the current model and the initial model to reduce the drift problem and
achieve stable tracking with significant changes in the target dimension. The
results show that the algorithm improves video tracking accuracy, simplifies
particle operation complexity, improves the speed, and has good
anti-interference ability and robustness.
