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

Abstract

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.

Description

Technical field: [0001] The invention belongs to the field of machine vision, and in particular relates to a random forest-based method for detecting key points of human faces partially occluded by ATM terminals. Background technique: [0002] The existing ATM video monitoring system is a passive defense system, the main function is to record the monitoring video, and after the event occurs, the video is used to collect evidence afterwards. This passive defensive approach may delay the best chance to resolve the incident, and even if the evidence is verified, the loss cannot be recovered. [0003] With the rapid development of my country's economy, ATM terminals have penetrated into people's lives. However, the social security situation is becoming more and more complicated, and the crime phenomenon on the ATM terminal is intensifying. The current situation has urgently required the field of ATM security to transform from traditional passive defense to active defense. [...

AI Technical Summary

Problems solved by technology

However, the existing mature face recognition technology mainly recognizes the complete face, and those who commit crimes against ATM terminals often consciously use masks, sunglasses and other objects to cover the face. Face information matching technology becomes difficult

Secondly, the background of the video surveillance image is complex, the face poses are diverse, and the key points of the face are difficult to determine, which further increases the difficulty of face positioning and identification

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