Research and Application of Video-based Face Recognition Algorithm in Rail Traffic Environment
With the development of biometrics, face recognition has become a hot topic in urban rail
transit security and AFC due to its unique advantages. More and more Internet and IT
companies in China are targeting the business opportunities of face recognition for Internet
finance and launching their own face recognition products. The purpose of this paper is to
solve the problem of how to effectively represent the face and how to reduce the influence of
external factors on face recognition. The face detection method based on Harr feature and
Adaboost is used to deeply study the existing face analysis algorithm. A more robust face
preprocessing algorithm and a description method based on LMTP for face texture features.
Then by studying the environment and passenger behavior characteristics of rail transit
stations, the method of judging the recognition ability of face recognition technology is
proposed. Finally, the feasibility of video-based face recognition technology in rail transit is
demonstrated through system testing. The research results show that the face preprocessing
algorithm combined with the features obtained by the LMTP operator and the LTP operator
can better describe the texture and have a higher recognition rate in face recognition. Noise
can be better handled.