Dynamic percentage feature cropping adaboost face detection algorithm

A face detection and percentage technology, applied in the field of pattern recognition, can solve problems such as large training time and consumption, and achieve the effect of saving training time

Inactive Publication Date: 2018-10-30
HOHAI UNIV
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art, provide a kind of dynamic percentage feature cutting AdaBoost face detection algorithm, solve the technical problem that the classifier that adopts AdaBoost algorithm training in the prior art can consume a large amount of training time

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  • Dynamic percentage feature cropping adaboost face detection algorithm
  • Dynamic percentage feature cropping adaboost face detection algorithm
  • Dynamic percentage feature cropping adaboost face detection algorithm

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] The meanings of each function in the accompanying drawings are as follows:

[0032] Function cvGetTickCount(): returns the number of milliseconds elapsed from the start of the operating system to the current time, and the time spent on training can be counted by calculating the difference between the two returned values.

[0033] Function Single_Classifier(int i): It is used to generate a strong classifier, and the parameter passed in indicates the number of weak classifiers constituting the strong classifier.

[0034] Function Generate_AllFeatures(int count): used to generate all Haar-like features, count represents the number of feature types used. The present invention selects 5 commonly used feature templates, so the count value is 5.

[0035] Function Input_Samples(): read positive and negative samples from the specified directory.

[0036] Function Sele...

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Abstract

The invention discloses a dynamic percentage feature clipping AdaBoost face detection algorithm, specifically: at the beginning of each iteration, first determine the percentage f of the number of features to be clipped, and then select features with better classification performance to participate in the next step rounds of training; when the error rate of the best weak classifier in this iteration obtained from training is greater than the random extraction value, the number of features participating in training is expanded by reducing the clipping coefficient of this iteration; if all features are used for training, If the error rate still exceeds 0.5, stop the iteration. The present invention is applicable to when there are too many features participating in the training, and the purpose of saving training time is achieved by selecting features with a lower error rate in the previous round to participate in the next round of training.

Description

technical field [0001] The invention relates to a dynamic percentage feature clipping AdaBoost human face detection algorithm, which belongs to the technical field of pattern recognition. Background technique [0002] Biometric identification technology is to achieve the purpose of identity verification or individual identification through the unique physiological and behavioral characteristics of each individual. As a kind of biometrics, the face is easy to obtain and has a friendly interface. Compared with the commonly used methods such as passwords, credit cards, and identity cards, it has the advantages of non-replicable, easy to carry, and strong identification. Therefore, it has broad prospects in the fields of video surveillance, smart home and criminal investigation. As the computing power of embedded devices becomes stronger and stronger, intelligent algorithms are more and more used in the field of embedded development to realize different functions. Among them, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/161G06F18/2413
Inventor 李东新左卜
Owner HOHAI UNIV
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