A design method of multi-pose face detector based on msnrd feature

Abstract

The invention discloses a design method of a multi-pose human face detector based on MSNRD features, including a training phase and a detection phase; in the training phase, the size of the template is first determined, and a training data set is prepared; secondly, a feature pool is constructed; and then in the Adaboost framework Next, train a strong classifier; in the detection stage, first traverse the sub-images of each scale in each position of the input image to determine whether it is a human face; all face images form a set, and the overlapping area of ​​any two elements in the set accounts for the smallest element area If the ratio exceeds 0.3, only keep the element with the larger output value of the classifier, and repeat this step until the ratio of the coincident area of ​​any two elements in the set to the area of ​​the smaller one does not exceed 0.3, and the sum of the set is regarded as the final result of the detection The result output. The method of the invention enriches the expression ability of the feature, simplifies the calculation process of the feature, avoids separately training detectors for different attitudes, reduces the workload of training, and improves the detection efficiency.

Description

technical field [0001] The invention relates to a design method of a multi-attitude human face detector, in particular to a design method of a multi-attitude human face detector based on MSNRD features. Background technique [0002] The face detector is the most basic step in the face recognition process, and its performance is directly related to the recognition accuracy of the recognition system. The main factors restricting its performance are: face posture, expression, and the richness and diversity of the environment. At present, the mainstream method of face detector is Haar or LBP features combined with Adaboost to form a strong classifier, and then multiple strong classifiers are cascaded to form a waterfall structure. LBP’s resistance to noise is weak, resulting in unstable detection results, which is not conducive to subsequent processing of faces; Haar features have two obvious defects: first, the expression ability is limited, limited to the expression of points...

AI Technical Summary

Problems solved by technology

LBP’s resistance to noise is weak, resulting in unstable detection results, which is not conducive to subsequent processing of faces; Haar features have two obvious defects: first, the expression ability is limited, limited to the expression of points, lines and edges, The face patterns are rich and diverse, so the expressive ability of the features is required to be strong; second, the calculation of the Haar feature requires integral graphs, square integral graphs, two additions, two divisions, and two root operations, which require a large amount of calculation and greatly Affected the detection speed

[0003] In order to improve the adaptability of the detector to face pose changes, expression changes and ambient light, the existing public technologies are to build different detectors for different poses, and the final result is the union of the detection results of multiple detectors; this exists Three flaws: First, faces are rich in variations, and it is difficult to classify and construct datasets, and the workload of classifying and constructing datasets is huge; second, face images with different poses, expressions, and lighting conditions have some common features. If the training is divided into categories, it is not conducive to make full use of these common features to improve the detection efficiency; third, since the detector scans one by one in different positions and different scales of the image in the form of a sliding window in actual use, if there are too many detectors , will inevitably affect the detection speed, which is not conducive to the application of the detector in real-time occasions

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