Face Detection Method Based on Convolutional Neural Network and Conditional Random Field

Abstract

The invention discloses a face detection method based on a convolutional neural network and a conditional random field model, which can improve the accuracy of the face frame determined by the network output layer. The present invention first trains the convolutional neural network to obtain a classifier for judging human faces and non-human faces, and performs sliding window face detection on the input image to obtain a window containing a human face; then mark all detections corresponding to the same human face Window, the confidence score corresponding to the window is used as the random variable of the conditional random field CRF, the correlation between the windows is calculated through the CRF model, and the window is selected according to the closeness of the correlation; finally, according to the size of the overlapping area and the horizontal and vertical distances The size of the overlap merges the windows of the same scale and different scales respectively to obtain the final face frame.

Description

technical field [0001] The invention belongs to the field of computer machine learning, and more specifically relates to a face detection method based on a convolutional neural network and a conditional random field. Background technique [0002] Face Detection technology is an important branch of machine learning. Its core idea is to first train a large number of face data samples to obtain a two-class classifier of a face, and then give a picture as input , through a certain strategy to select any area in the picture for discrimination, frame the area that is the face, until all areas of the picture are discriminated. [0003] At present, face detection methods mainly include cascade classifier (Cascade), regional feature detection method DPM, and convolutional neural network detection method, among which convolutional neural network detection method is the mainstream method widely used in recent years. [0004] The basic principle of convolutional neural network is to tr...

AI Technical Summary

Problems solved by technology

These algorithms have good performance for frontal faces, but they are still not enough to deal with partial faces, expressive faces and occluded faces, and the accuracy rate is low when dealing with different angles, different expressions and occluded faces.

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