FACE DETECTION METHOD AND APPARATUS, COMPUTER DEVICE, AND STORAGE MEDIUM
FIG11 is a flowchart of calculating an error of a divided sample image at a node according to an embodiment  FIG10 is a flowchart of determining a division pixel pair of each of nodes according to an embodiment
As shown in FIG11, in an embodiment, S1002, that is, repeating for the first preset number of times randomly selecting a pixel pair from the second pixel pair collection to divide the sample images allocated to the node, split the node into new branch nodes, determine branch nodes where the divided sample images are located, and calculate errors of the divided sample images at the node, includes the followings:S 1102: Obtain coordinate information of a pixel pair selected randomly from the second pixel pair collection.  As shown in FIG10, in an embodiment, S904 includes the followings:S1002: Looping or iterating for a first preset number of times: randomly selecting a pixel pair from the second pixel pair collection to divide the sample images allocated to the node, split the node into new branch nodes, determine branch nodes where the divided sample images are located, and calculate errors of the divided sample images at the node.
flowchart
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node nodes
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such as
['1. A face detection method for a computer device, comprising: obtaining a to-be-detected image; obtaining an initial shape of a current regression tree in a pre-constructed probability regression model; extracting image features from the to-be-detected image and respectively calculating a probability of each of leaf nodes of the current regression tree according to the image features; extracting an error of each of the leaf nodes from the current regression tree; determining a shape error of the current regression tree according to the probability and the error of each of the leaf nodes; calculating, by the compute device, an estimated shape of the current regression tree according to the initial shape and the shape error; and performing iterative calculation by using the estimated shape as an initial shape of a neighboring next regression tree until a last regression tree in the probability regression model, to obtain an estimated shape of the last regression tree as a detected face shape.']