An anti-counterfeiting face detection method, wherein the method is applied to a multi-lens camera comprising a Time-Of-Flight, TOF, camera and a Red-Green-Blue, RGB, camera, and comprises: acquiring a depth image, an infrared image and an RGB image by using the TOF camera and the RGB camera; analyzing the RGB image through a preset face detection algorithm to determine an RGB face region of a face in the RGB image and position information of the RGB face region; determining a depth face region of the face in the depth image and an infrared face region of the face in the infrared image based on the position information of the RGB face region; and determining that the face passes the detection when the depth face region, the infrared face region and the RGB face region meet corresponding preset rules respectively, which comprises: calculating a similarity between the depth face region and a reserved priori face template; determining a first image feature of the infrared face region, and analyzing the first image feature through a pre-trained first deep learning model to obtain an infrared analysis result; determining a second image feature of the RGB face region, and analyzing the second image feature through a pre-trained second deep learning model to obtain an RGB analysis result; and determining that the face passes the detection, when the similarity is greater than a preset similarity threshold, the infrared analysis result indicates that the face is a living body, and the RGB analysis result indicates that the face is a living body; and wherein calculating the similarity between the depth face region and the reserved priori face template comprises: determining a side view and a top view of the face based on the depth face region; and comparing a face depth change curve of the side view and a face depth change curve of the top view with a face depth change curve of the reserved priori face template to determine the similarity between the depth face region and the reserved priori face template. acquiring a depth image, an infrared image and an RGB image by using the TOF camera and the RGB camera; analyzing the RGB image through a preset face detection algorithm to determine an RGB face region of a face in the RGB image and position information of the RGB face region; determining a depth face region of the face in the depth image and an infrared face region of the face in the infrared image based on the position information of the RGB face region; and determining that the face passes the detection when the depth face region, the infrared face region and the RGB face region meet corresponding preset rules respectively, which comprises: calculating a similarity between the depth face region and a reserved priori face template; determining a first image feature of the infrared face region, and analyzing the first image feature through a pre-trained first deep learning model to obtain an infrared analysis result; determining a second image feature of the RGB face region, and analyzing the second image feature through a pre-trained second deep learning model to obtain an RGB analysis result; and determining that the face passes the detection, when the similarity is greater than a preset similarity threshold, the infrared analysis result indicates that the face is a living body, and the RGB analysis result indicates that the face is a living body; and wherein calculating the similarity between the depth face region and the reserved priori face template comprises: determining a side view and a top view of the face based on the depth face region; and comparing a face depth change curve of the side view and a face depth change curve of the top view with a face depth change curve of the reserved priori face template to determine the similarity between the depth face region and the reserved priori face template. calculating a similarity between the depth face region and a reserved priori face template; determining a first image feature of the infrared face region, and analyzing the first image feature through a pre-trained first deep learning model to obtain an infrared analysis result; determining a second image feature of the RGB face region, and analyzing the second image feature through a pre-trained second deep learning model to obtain an RGB analysis result; and determining that the face passes the detection, when the similarity is greater than a preset similarity threshold, the infrared analysis result indicates that the face is a living body, and the RGB analysis result indicates that the face is a living body; and wherein calculating the similarity between the depth face region and the reserved priori face template comprises: determining a side view and a top view of the face based on the depth face region; and comparing a face depth change curve of the side view and a face depth change curve of the top view with a face depth change curve of the reserved priori face template to determine the similarity between the depth face region and the reserved priori face template. determining a side view and a top view of the face based on the depth face region; and comparing a face depth change curve of the side view and a face depth change curve of the top view with a face depth change curve of the reserved priori face template to determine the similarity between the depth face region and the reserved priori face template.