CN105427046A - Facial makeup identification method for cigarette quality features - Google Patents

Abstract

The invention discloses a facial makeup identification method for cigarette quality features. The method comprises: step 1, obtaining sensory evaluation data; step 2, reading the sensory evaluation data of cigarettes into Matlab software, performing data preprocessing, and drawing a facial makeup graph according to a mapping table of the cigarette quality features and facial features; step 3, drawing cigarette sample facial makeup graphs for the preprocessed data; step 4, classifying samples according to the similarity among the cigarette sample facial makeup graphs; and step 5, comparing and observing various facial makeup graphs, analyzing prominent features of various facial makeup graphs, and determining main body quality features of various samples. The method has the advantages that the facial makeup graphs are vivid and visual; the cigarette quality features can be visualized into the facial features very vividly; and the similarity of quality features of cigarette products is visually analyzed to meet the demands of comparing and analyzing the cigarette products.

Description

A kind of face recognition method of cigarette quality feature

Technical field

The invention belongs to cigarette sensory evaluation technique field, be specifically related to a kind of face recognition method of cigarette quality feature.

Background technology

Cigarette is a kind of specialities for people's consumption.Because the final consumption form of cigarette product is sucked exactly, the style and features of cigarette and fragrance quality will could provide impression to consumer through burning, what therefore, the most directly, the most in time, can represent consumer's will is exactly organoleptic analysis to cigarette and evaluation.The overall tasks of cigarette organoleptic analysis and evaluation and object judge exactly and describe fragrance quality level and style characteristic, be also for R&D personnel, marketing personnel provide effectively, information reliably, to make correct product and marketing decision.Due to the demand in market and the development of product, the A+E of cigarette has become tobacco business important field of research and technical work.

For the evaluation of cigarette quality feature, tobacco business establishes corresponding evaluation criterion YC/T497-2014 " cigarette Chinese-style cigarette style sensory evaluation method ", apply the method that this standard specifies and can carry out quantitative evaluation to 12 of a cigarette qualitative characteristics index, also histogram can be adopted to obtain corresponding evaluation result figure, but be difficult to the similarity analyzing different product qualitative characteristics according to quantitative result and histogram intuitively.

For the problems referred to above, how adopting data visualization method, analyze intuitively the qualitative characteristics of cigarette product, to meet the demand of cigarette product quality comparative analysis, is the technical barrier that this area waits to solve.

Summary of the invention

The object of the invention is to for the deficiencies in the prior art part, a kind of cigarette quality feature visualization method is provided; Thus cigarette Analyses Methods for Sensory Evaluation Results is vivo represented in face graph mode, can judge that the place similar in qualitative characteristics of each cigarette sample shows in which intuitively according to the shape of the types of facial makeup in Beijing operas, difference table is which aspect now.

Object of the present invention is achieved by following technical proposals:

1. obtain sensory evaluation data

The valuation officer of cigarette sensory evaluation adopts YC/T497-2014 " cigarette Chinese-style cigarette style sensory evaluation method " to evaluate p qualitative characteristics of N number of cigarette sample;

2. the cigarette sensory evaluation data of the p of a N number of cigarette sample qualitative characteristics is read in Matlab software, qualitative characteristics sequencing according to cigarette quality feature and facial feature map table (table 1) adjusts former characteristic sequence, line number of going forward side by side Data preprocess;

After adjusting characteristic sequence according to table 1, obtain data variable

$X=\left(\begin{array}{cccc}{x}_{11}& x_{12}& \mathrm{...}& x_{1p}\\ x_{21}& x_{22}& \mathrm{...}& x_{2p}\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ x_{N1}& x_{N2}& \mathrm{...}& x_{Np}\end{array}\right),$

Following normalization transfer pair data X is adopted to carry out pre-service:

${x^{*}}_{ik}=\frac{x_{ik}-x_{min,k}}{x_{max,k}-x_{\min ,k}}(b-a)+a,i=1,...,N,k=1,...,p,$ X in formula _ikbe the raw data of the kth qualitative characteristics index of the i-th sample, x ^* _ikrepresent the data after conversion, x _{max, k}=max _1≤i≤Nx _ik, x _{min, k}=min _1≤i≤Nx _ik, a=0.05, b=0.95;

3. couple pretreated data X ^*call Matlab function glyphplot and draw cigarette sample face graph, input parameter ' standardize ' value ' off ' of glyphplot;

4. according to the similarity between cigarette sample face graph to sample classification, and call Matlab function glyphplot and draw sub-category sample face graph;

5. comparative observation face graph, analyzes the prominent feature of all kinds of types of facial makeup in Beijing operas, and according to cigarette quality characteristic index and facial feature map table, i.e. table 1, determines the main body qualitative characteristics of all kinds of sample.

The invention has the beneficial effects as follows: face graph is lively, directly perceived, very visually cigarette quality feature can be converted into face feature, utilize the mankind to the psychic trait of face feature slight change sensitivity, the similarity of the qualitative characteristics of cigarette product and dissimilarity are identified, intuitively to meet the demand of cigarette product comparative analysis.

Accompanying drawing explanation

Fig. 1 is 15 cigarette sample face graphs of the face recognition method of a kind of cigarette quality feature of the present invention;

Fig. 2 is 15 cigarette sample face graphs of the sub-category display of the face recognition method of a kind of cigarette quality of the present invention feature;

Fig. 3 is the face recognition method operating process schematic diagram of a kind of cigarette quality feature of the present invention.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with the drawings and specific embodiments, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.

Consult shown in Fig. 3, the inventive method chooses the sensory evaluation data of 15 Chinese-style cigarette samples as embodiment, and concrete steps are consistent with step described in summary of the invention part.

1. by sensory evaluation expert to N=15 cigarette sample, the evaluating data of p=12 qualitative characteristics, i.e. table 1, read in Matlab software, according to the qualitative characteristics sequencing of cigarette quality feature and facial feature map table (table 1), former characteristic sequence is adjusted, obtain data variable X, adopt following normalization transfer pair data X to carry out pre-service:

${x^{*}}_{ik}=\frac{x_{ik}-x_{\min ,k}}{x_{max,k}-x_{\min ,k}}(b-a)+a,i=1,...,N,k=1,\mathrm{...},p,$

X in formula _ikbe the raw data of the kth qualitative characteristics index of the i-th sample, x ^* _ikrepresent the data after conversion, x _{max, k}=max _1≤i≤Nx _ik, x _{min, k}=min _1≤i≤Nx _ik, a=0.05, b=0.95.

3. couple pretreated data X ^*call Matlab function glyphplot and draw cigarette sample face graph, i.e. Fig. 1.

Perform Matlab order:

N={' sample 1', ' sample 2' ..., ' sample N'};

glyphplot(X ^*,'standardize','off','ObsLabels',n,'glyph','face')

The input parameter of glyphplot ' standardize' value ' off', glyph' value that input parameter uses ' ObsLabels' can make to increase the concrete title n of sample below each face graph, input parameter ' ' face' draws face graph.

Observation Fig. 1 is known: sample 1, sample 2, and the face of sample 15, eyes, nose are all more similar, so these 3 samples can be divided into a class (class 1); Sample 3, sample 4, sample 10, the shape of face of sample 11, eyes, nose are more similar, so these 4 samples can be divided into a class (class 2); Sample 5, sample 7, sample 9, sample 12, the shape of face of sample 13 and sample 14, eyes, nose are more similar, and sample 6 is still comparatively similar with the types of facial makeup in Beijing operas and this six samples of sample 8 by contrast, so these 8 samples can be divided into a class (class 3).

4. according to the similarity between cigarette sample face graph to sample classification, and call Matlab function glyphplot and draw sub-category sample face graph, i.e. Fig. 2.

Face graph can be divided into l=3 class as shown in Figure 1.Note X _j ^*for jth class sample data, n _jfor jth class sample ID;

Perform Matlab order:

forj＝1:l

figure(j)；

glyphplot(X _j ^*,'standardize','off','ObsLabels',n _j,'glyph','face')；

end

The input parameter of glyphplot ' standardize' value ' off'.

5. the face graph that comparative observation is all kinds of, analyzes the prominent feature of all kinds of types of facial makeup in Beijing operas, and according to cigarette quality feature and facial feature map table (see table 1), determines the main body qualitative characteristics of all kinds of sample.

Observation Fig. 1 is known: in general, in class 1, the face of each sample is larger, second appearance upper is to longer, wider between two, according to qualitative characteristics and facial feature map table (see table 1), these 3 samples known are with oral stimulation/tongue calcination, and oral cavity remains/dry sensation, and throat's drying is main quality feature.In class 2, the face of each sample has a certain size, and the distance between two is narrower, and eye-level is less, is with oral stimulation/tongue calcination according to table 1 these 4 samples known, and throat's drying and fragrance are main quality feature.In class 3, the face of each sample all has some features and face shape is more outstanding, the oval eccentricity of second face upper is less, distance between two is narrower, the angle (affecting the angle of eyebrow) of eyes is comparatively large, and the angle (relative to eyes) of eyebrow is comparatively large, according to table 1 known they mainly with oral stimulation/tongue calcination, convergence, throat stimulates as main body quality characteristic, is aided with certain throat dry, exquisiteness/soft/mellow and full and strength.

Table 2: raw data

The invention has the beneficial effects as follows: face graph is lively, directly perceived, very visually cigarette quality feature can be converted into face feature, utilize the mankind to the psychic trait of face feature slight change sensitivity, the similarity of the qualitative characteristics of cigarette product and dissimilarity are identified, intuitively to meet the demand of cigarette product comparative analysis.

The above, be only the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, and any change of expecting without creative work or replacement, all should be encompassed in the new protection domain of this practicality.

Claims (3)

1. a face recognition method for cigarette quality feature, is characterized in that, comprise the following steps:

Step one, acquisition sensory evaluation data: the valuation officer of cigarette sensory evaluation adopts YC/T497-2014 " cigarette Chinese-style cigarette style sensory evaluation method " to evaluate the qualitative characteristics of cigarette sample;

Step 2, cigarette sensory evaluation data is read in Matlab software, the sequencing according to cigarette quality feature and facial feature map table adjusts former characteristic sequence, line number of going forward side by side Data preprocess;

Step 3, cigarette sample face graph is drawn to pretreated data call Matlab function glyphplot;

Step 4, according to the similarity between cigarette sample face graph to sample classification, and call Matlab function glyphplot and draw sub-category sample face graph;

Step 5, comparative observation face graph, analyze the prominent feature of all kinds of types of facial makeup in Beijing operas, and according to cigarette quality feature and facial feature map table, determine the main body qualitative characteristics of all kinds of sample;

12 of cigarette qualitative characteristics indexs and face features are comprised the size of face by the method, the position of the length of nose and face, eyes, eyebrow etc. maps one by one.

2. the face recognition method of cigarette quality feature as claimed in claim 1, it is characterized in that, the data preprocessing method of described step 2 is:

Following normalization is adopted to convert to the sensory evaluation data of N number of sample p qualitative characteristics index: ${x^{*}}_{ik}=\frac{x_{ik}-x_{min,k}}{x_{max,k}-x_{min,k}}(b-a)+a,i=1,...,N,k=1,...,p,$ X in formula _ikbe the raw data of the kth qualitative characteristics index of the i-th sample, x ^* _ikrepresent the data after conversion, x _{max, k}=max _1≤i≤Nx _ik, x _{min, k}=min _1≤i≤Nx _ik, a=0.05, b=0.95.

3. the face recognition method of cigarette quality feature as claimed in claim 1 or 2, it is characterized in that, the cigarette quality feature of described step 2 and step 5 and facial feature map table are in table 1:

Table 1: cigarette quality feature and facial feature map table

Qualitative characteristics index due to Chinese-style cigarette is 12, and the face feature of mapping in Matlab is 17, does not have 5 corresponding face features automatically to get default value when Matlab draws face graph.