A face liveness detection method based on a neural network model, comprising: obtaining a target visible light image and a target infrared image of a target object to be detected; extracting, from the target visible light image, a first face image containing only a face part; extracting, from the target infrared image, a second face image containing only the face part; generating a target image array of the target object by using multiple monochromatic components of the first face image and a monochromatic component of the second face image; and feeding the target image array into a neural network model pre-trained for detection, to obtain a face liveness detection result of the target object, characterised in that generating the target image array of the target object by using the multiple monochromatic components of the first face image and the monochromatic component of the second face image comprises: performing dimensionality reduction on a combination of the multiple monochromatic components of the first face image and the monochromatic component of the second face image to obtain a target grayscale image; and using one-dimensional data corresponding to the target grayscale image to constitute the target image array of one-dimension, wherein performing dimensionality reduction comprises: for each of pixels, performing weighted averaging on component data of the pixel in each monochromatic component. obtaining a target visible light image and a target infrared image of a target object to be detected; extracting, from the target visible light image, a first face image containing only a face part; extracting, from the target infrared image, a second face image containing only the face part; generating a target image array of the target object by using multiple monochromatic components of the first face image and a monochromatic component of the second face image; and feeding the target image array into a neural network model pre-trained for detection, to obtain a face liveness detection result of the target object, characterised in that generating the target image array of the target object by using the multiple monochromatic components of the first face image and the monochromatic component of the second face image comprises: performing dimensionality reduction on a combination of the multiple monochromatic components of the first face image and the monochromatic component of the second face image to obtain a target grayscale image; and using one-dimensional data corresponding to the target grayscale image to constitute the target image array of one-dimension, wherein performing dimensionality reduction comprises: for each of pixels, performing weighted averaging on component data of the pixel in each monochromatic component. performing dimensionality reduction on a combination of the multiple monochromatic components of the first face image and the monochromatic component of the second face image to obtain a target grayscale image; and using one-dimensional data corresponding to the target grayscale image to constitute the target image array of one-dimension, wherein performing dimensionality reduction comprises: for each of pixels, performing weighted averaging on component data of the pixel in each monochromatic component.