Algorithm 1: WMHS.

Input: mfcc-final←[feat,defeat,dttfeat]

Output: verification results

1: img.power←mfcc−final.img(255*255);

2: G←gradient_magnitude←$\sqrt{G_x^2\left(x,y\right)+G_y^2(x,y)}$; // Calculate the total gradient value

3: Angle←$\alpha \left(x,y\right)={\tan }^{-1}\frac{G_y\left(x,y\right)}{G_x\left(x,y\right)}$;

4: function UPDATEBINS(self,G, Angle)

5: while dividing the image into cells do

6: bins←zeros((cell_G.shape[0], cell_G.shape[1],self.bin_count));

7: for i = range(bins.shape[0])

8: for j = range(bins.shape[1])

9: bins←tmp_unit←self.bin_count; // Vote for each gradient direction

10: end for

11: end for

12: end while

13: return bins;

14: end function

15: block←bins.feature

16: $F^{HOG}=\left(F_1^{HOG},F_2^{HOG},\cdots ,F_N^{HOG}\right)$ ← block

17: clf = svm. SVC(); // training model

18: clf.fit(train_ reduction, train_ target);

19: classification function ← $f(x)=\mathrm{sgn}\left\{\left({\displaystyle \sum _{i=1}^n{a}_j{}^{\ast }{y}_j(p_j·q_i)}\right)+b^{\ast }\right\},p\in R^n$

20: pred = clf.predict(test_reduction) // predict

21: return precision