CN105005768B - Dynamic percentage sample cuts AdaBoost method for detecting human face - Google Patents

Dynamic percentage sample cuts AdaBoost method for detecting human face Download PDF

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CN105005768B
CN105005768B CN201510391152.3A CN201510391152A CN105005768B CN 105005768 B CN105005768 B CN 105005768B CN 201510391152 A CN201510391152 A CN 201510391152A CN 105005768 B CN105005768 B CN 105005768B
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sample
training
error rate
weak classifier
samples
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CN105005768A (en
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李东新
张鸿鹏
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Hohai University HHU
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Hohai University HHU
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification

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  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
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Abstract

The invention discloses a kind of dynamic percentage samples to cut AdaBoost Face datection algorithms, specially：The percentage f of number of samples is cut needed for being determined first at the beginning of each iteration, each round crops the smaller sample of weight according to f, it is trained with remaining sample, when the best Weak Classifier error rate for the current iteration that training obtains is more than the error rate that random value generates, by reducing the constant f cut, enlarged sample collection quantity, training is re-started for current iteration.If when being trained using whole samples, error rate stops iteration still above 0.5.When the present invention is excessive suitable for the number of samples for participating in training, by selected part to the better sample of performance boost effect, to achieve the purpose that save the training time.

Description

Dynamic percentage sample cuts AdaBoost method for detecting human face
Technical field
The present invention relates to a kind of dynamic percentage samples to cut AdaBoost method for detecting human face, belongs to mode identification technology Field.
Background technology
Biometrics identification technology is to realize identity card by each individual exclusive physiological characteristic and behavioural characteristic The purpose that real or individual differentiates.The features such as one kind of face as biological characteristic has and is easily obtained, and interface is friendly, compared to Existing frequently-used mode, such as password, credit card, ID card have the advantages such as not reproducible, easy to carry, distinctive is strong.Cause This has broad prospects in fields such as video monitoring, smart home and criminal investigations.As embedded device operational capability is got over Come stronger, intelligent algorithm is increasingly being applied to embedded development field, realizes different functions.Wherein Face datection conduct The basis of recognition of face becomes the research hotspot of artificial intelligence field.
Its core of AdaBoost algorithms is to extract classifying quality most from a large amount of Haar features by the method for iteration Good feature is as Weak Classifier, and the strong classifier ultimately generated is made of a large amount of Weak Classifier.AdaBoost is practical And it is simple, and the method for detecting human face based on AdaBoost algorithms not only has high inspection for the detection of single facial image Precision is surveyed, and has detection speed quickly, therefore the face recognition technology based on the algorithm is widely used.
Work as training sample, sample characteristics, when Weak Classifier number is more, using the classification of AdaBoost algorithms training Device can consume a large amount of training time.Characteristic Number determines that the iterations of algorithm, each iteration obtain individual features and instructing Practice the error rate in sample set, best Weak Classifier is obtained finally by comparison error rate.A best weak typing is often trained The weight of device, training sample can change accordingly, so if needing more Weak Classifiers, then need to repeat corresponding time Several above-mentioned steps.It can be seen that work as training sample, and when sample characteristics number and Weak Classifier number increase, training time meeting Increased with the three cubed order of magnitude.
Invention content
It is an object of the invention to overcome deficiency in the prior art, a kind of dynamic percentage sample cutting is provided AdaBoost method for detecting human face solves to use the grader of AdaBoost algorithms training that can consume a large amount of instruction in the prior art The technical issues of practicing the time.
In order to solve the above technical problems, the technical solution adopted in the present invention is：Dynamic percentage sample is cut AdaBoost method for detecting human face, at the beginning of each iteration, it is first determined the required percentage f for cutting number of samples, Each round crops the smaller sample of weight according to f, is trained with remaining sample；
When training the best Weak Classifier error rate of obtained current iteration to be more than the error rate that random value generates, by subtracting The constant f of small cutting, enlarged sample collection quantity, training is re-started for current iteration；
If when being trained using whole samples, error rate then stops iteration still above 0.5；
Specific algorithm includes the following steps：
Step 1：If the training sample sum of input is N, wherein negative sample is m, and positive sample is n, training sample set For S={ (x1,y1),...(xn,yn), wherein xiIndicate i-th of sample, yi={ 1,0 } is respectively used to identify positive negative sample；
Step 2：Initialization sample weight：
Step 3：Assuming that the sample percentage that each round is cast out is f, then the number of samples that each round participates in training is N × (1-f), iterations t=1,2 ..., T；
Step 4：Optimal Weak Classifier is obtained, Weak Classifier h is acquiredtWeighting coefficient α in strong classifiert, method is such as Under：
Step 401：Normalize the weighted value of sample：
Step 402：For each feature j, one simple Weak Classifier h of trainingj(x,fj,pj,θj)：
Wherein, fj(x) value, p are characterizedjIndicate sign of inequality direction, θjFor Weak Classifier threshold value；
Step 403：Select the corresponding Weak Classifier h of minimal error ratet(x), wherein minimal error rate is defined as：
Step 404：If εt=0 or occur as soon as ε when training the first roundt>=0.5, then T=t-1 is enabled, step is jumped to Six；If εt>=0.5 and be not the first round, then T=t-1 is enabled, judges whether f is more than 2/3, it is no if more than f=2 × f-1 is then enabled Then f=f/2 is enabled to jump to step 5；
Step 405：Update sample weights：
As sample xiE when being classified by mistakei=0, on the contrary ei=1,
Step 406：Acquire Weak Classifier htWeighting coefficient in strong classifier：
Step 5：It to sample in training set, is arranged by weighted value, according to the percentage f of cutting, is cut from small to large Fall the smaller preceding n × f sample of weight；
Step 6：Export strong classifier：
Compared with prior art, the advantageous effect of the invention reached is：Number of samples suitable for participating in training is excessive When, by selected part to the better sample of performance boost effect, to achieve the purpose that save the training time.
Description of the drawings
Fig. 1 is the flow chart of the method for the present invention.
Fig. 2 is the flow chart for obtaining optimal Weak Classifier.
Specific implementation mode
The invention will be further described below in conjunction with the accompanying drawings.
Meaning in attached drawing represented by each function is as follows：
Function cvGetTickCount ()：It returns from os starting to the millisecond number currently passed through, passes through calculating The difference of two back amount can count the training spent time.
Function Single_Classifier (int i)：For generating a strong classifier, incoming parameter indicates to constitute The Weak Classifier number of this strong classifier.
Function Generate_AllFeatures (int count)：Feature for generating all Haar-like, count Indicate the quantity using characteristic type.The present invention has selected 5 kinds of common feature templates, therefore count values are 5.
Function Input_Samples ()：Positive negative sample is read in from specified directory.
Function Select_WeakClassifier ()：For obtaining optimal Weak Classifier.
Function Output_WeakClassifier ()：For exporting the Weak Classifier generated.
Function Cal_HaarValue (j, k)：J-th of feature for calculating k-th of sample.
Function qsort ()：Sample is ranked up according to the size of characteristic value.
As shown in Figure 1, dynamic percentage sample cuts AdaBoost method for detecting human face, when each iteration starts It waits, it is first determined the required percentage f for cutting number of samples, each round crops the smaller sample of weight according to f, with remaining sample Originally it is trained；
When training the best Weak Classifier error rate of obtained current iteration to be more than the error rate that random value generates, by subtracting The constant f of small cutting, enlarged sample collection quantity, training is re-started for current iteration；
If when being trained using whole samples, error rate then stops iteration still above 0.5；
Specific algorithm includes the following steps：
Step 1：If the training sample sum of input is N, wherein negative sample is m, and positive sample is n, training sample set For S={ (x1,y1),...(xn,yn), wherein xiIndicate i-th of sample, yi={ 1,0 } is respectively used to identify positive negative sample；
Step 2：Initialization sample weight：
Step 3：Assuming that the sample percentage that each round is cast out is f, then the number of samples that each round participates in training is N × (1-f), iterations t=1,2 ..., T；
Step 4：Optimal Weak Classifier is obtained, Weak Classifier h is acquiredtWeighting coefficient α in strong classifiert, such as Fig. 2 institutes Show, method is as follows：
Step 401：Normalize the weighted value of sample：
Step 402：For each feature j, one simple Weak Classifier h of trainingj(x,fj,pj,θj)：
Wherein, fj(x) value, p are characterizedjIndicate sign of inequality direction, θjFor Weak Classifier threshold value；
Step 403：Select the corresponding Weak Classifier h of minimal error ratet(x), wherein minimal error rate is defined as：
Step 404：If εt=0 or occur as soon as ε when training the first roundt>=0.5, then T=t-1 is enabled, step is jumped to Six；If εt>=0.5 and be not the first round, then T=t-1 is enabled, judges whether f is more than 2/3, it is no if more than f=2 × f-1 is then enabled Then f=f/2 is enabled to jump to step 5；
Step 405：Update sample weights：
As sample xiE when being classified by mistakei=0, on the contrary ei=1,
Step 406：Acquire Weak Classifier htWeighting coefficient in strong classifier：
Step 5：It to sample in training set, is arranged by weighted value, according to the percentage f of cutting, is cut from small to large Fall the smaller preceding n × f sample of weight；
Step 6：Export strong classifier：
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations Also it should be regarded as protection scope of the present invention.

Claims (1)

1. dynamic percentage sample cuts AdaBoost method for detecting human face, which is characterized in that at the beginning of each iteration, The percentage f of number of samples is cut needed for determining first, each round crops the smaller sample of weight according to f, with remaining sample It is trained；
When training the best Weak Classifier error rate of obtained current iteration to be more than the error rate that random value generates, passes through to reduce and cut out The constant f cut, enlarged sample collection quantity, training is re-started for current iteration；
If when being trained using whole samples, error rate then stops iteration still above 0.5；
Specific algorithm includes the following steps：
Step 1：If the training sample sum of input is N, wherein negative sample is m, and positive sample is n, training sample set S ={ (x1,y1),...(xn,yn), wherein xiIndicate i-th of sample, yi={ 1,0 } is respectively used to identify positive negative sample；
Step 2：Initialization sample weight：
Step 3：Assuming that the sample percentage that each round is cast out is f, then the number of samples that each round participates in training is N × (1- F), iterations t=1,2 ..., T；
Step 4：Optimal Weak Classifier is obtained, Weak Classifier h is acquiredtWeighting coefficient α in strong classifiert, method is as follows：
Step 401：Normalize the weighted value of sample：
Step 402：For each feature j, one simple Weak Classifier h of trainingj(x,fj,pj,θj)：
Wherein, fj(x) value, p are characterizedjIndicate sign of inequality direction, θjFor Weak Classifier threshold value；
Step 403：Select the corresponding Weak Classifier h of minimal error ratet(x), wherein minimal error rate is defined as：
Step 404：If εt=0 or occur as soon as ε when training the first roundt>=0.5, then T=t-1 is enabled, step 6 is jumped to；Such as Fruit εt>=0.5 and be not the first round, then T=t-1 is enabled, judges whether f is more than 2/3, if more than f=2 × f-1 is then enabled, otherwise enables f =f/2 jumps to step 5；
Step 405：Update sample weights：
As sample xiE when being classified by mistakei=0, on the contrary ei=1,
Step 406：Acquire Weak Classifier htWeighting coefficient in strong classifier：
Step 5：It to sample in training set, is arranged from small to large by weighted value, according to the percentage f of cutting, crops power The smaller preceding n × f sample of weight；
Step 6：Export strong classifier：
CN201510391152.3A 2015-07-06 2015-07-06 Dynamic percentage sample cuts AdaBoost method for detecting human face Expired - Fee Related CN105005768B (en)

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CN106022225B (en) * 2016-05-10 2019-03-05 中科天网（广东）科技有限公司 A kind of Face datection classifier building method based on AdaBoost
CN106951930A (en) * 2017-04-13 2017-07-14 杭州申昊科技股份有限公司 A kind of instrument localization method suitable for Intelligent Mobile Robot
CN107477809A (en) * 2017-09-20 2017-12-15 四川长虹电器股份有限公司 Air conditioner energy source management system based on Adaboost

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