ATM terminal human face key points partially shielding detection method based on random forest

The invention discloses an ATM terminal human face key points partially shielding detection method based on a random forest comprises the following steps: acquiring an image when a bank card is inserted into an ATM, improving the gray image contrast degree through a multi-scale Retinex enhancing method, positioning a human face through adoption of a Haarcascade human face detection algorithm, speculating a human face gesture based on a gesture estimation tree model of the random forest, judging a human face shielding type through a Viola Jones frame, and, according to the estimated human face gesture, selecting a random forest key point detection model and calibrating a human face key point of a position which is not shielded. An influence of the gesture on human face key point detection is reduced through gesture pre-estimation based on the random forest, an influence of shielding on a human face characteristic is reduced through automatic recognition of a shielding portion and key point marking of a non-shielding portion, a high-precision human face local characteristic can be provided for ATM-based identity recognition of a shielded human face, and human face identity recognition precision is improved.