How to interpret the confusion matrix and compare the result of features extraction with LBP and Haralick

I’m begginer in deep learning so I tried to execute a code of liveness face detection from github in this link :https://github.com/imironica/liveness , so when I tried to run features extraction with LBP and HAralik, I got a multiple results in confusion matrixes like this:
1- test features with LBP:

Train Nearest neighbors (3)
Accuracy: 0.7621621621621621
Confusion matrix:
 [[1625   45]
 [ 703  772]]

Train SGD
Accuracy: 0.8158982511923688
Confusion matrix:
 [[1588   82]
 [ 497  978]]

Train Naive Bayes
Accuracy: 0.8756756756756757
Confusion matrix:
 [[1544  126]
 [ 265 1210]]

Train Decision Tree Classifier 
Accuracy: 0.6791732909379968
Confusion matrix:
 [[1569  101]
 [ 908  567]]

Train Adaboost Classifier 
Accuracy: 0.6515103338632751
Confusion matrix:
 [[1630   40]
 [1056  419]]

Train Gradient Boosting Classifier
Accuracy: 0.6419713831478537
Confusion matrix:
 [[1622   48]
 [1078  397]]

Train Random Forest Classifier
Accuracy: 0.7106518282988871
Confusion matrix:
 [[1641   29]
 [ 881  594]]

Train Extremelly Trees Classifier
Accuracy: 0.724006359300477
Confusion matrix:
 [[1638   32]
 [ 836  639]]


Train Linear SVM with C=1 
Accuracy: 0.8076311605723371
Confusion matrix:
 [[1593   77]
 [ 528  947]]

Train SVM with C=10 
Accuracy: 0.8082670906200318
Confusion matrix:
 [[1591   79]
 [ 524  951]]

2-and when I tried test features with Haralik I got:

Train Nearest neighbors (3)

Nearest neighbors (3): 0.753577106518283

[[1580   90]

 [ 685  790]]

Train SGD

SGD: 0.7424483306836248

[[1598   72]

 [ 738  737]]

Train Naive Bayes

Naive Bayes: 0.7093799682034976

[[1521  149]

 [ 765  710]]

Train Decision Tree Classifier 

Decision Tree Classifier : 0.6511923688394277

[[1527  143]

 [ 954  521]]

Train Adaboost Classifier 

Adaboost Classifier : 0.763751987281399

[[1537  133]

 [ 610  865]]

Train Gradient Boosting Classifier

Gradient Boosting Classifier: 0.7939586645468999

[[1544  126]

 [ 522  953]]

Train Random Forest Classifier

Random Forest Classifier: 0.7007949125596185

[[1564  106]

 [ 835  640]]

Train Extremelly Trees Classifier

Extremelly Trees Classifier: 0.7364069952305247

[[1543  127]

 [ 702  773]]

Train Linear SVM with C=0.01 

Linear SVM with C=0.01 : 0.5303656597774244

Train Linear SVM with C=0.1 

Linear SVM with C=0.1 : 0.729093799682035

Train Linear SVM with C=1 

Linear SVM with C=1 : 0.7335453100158983

Train Linear SVM with C=10 

Linear SVM with C=10 : 0.7869634340222575

Train Linear SVM with C=100 

Linear SVM with C=100 : 0.7939586645468999

Train Linear SVM with C=500 

Linear SVM with C=500 : 0.7936406995230525

Train Linear SVM with C=1000 

Linear SVM with C=1000 : 0.7968203497615263

Train Linear SVM with C=2000 

Linear SVM with C=2000 : 0.794912559618442

Train SVM with C=0.01 

SVM with C=0.01 : 0.5310015898251192

Train SVM with C=0.1 

SVM with C=0.1 : 0.5306836248012718

Train SVM with C=1 

SVM with C=1 : 0.7297297297297297

Train SVM with C=10 

SVM with C=10 : 0.7456279809220986

Train SVM with C=100 

SVM with C=100 : 0.7802861685214626

Train SVM with C=500 

SVM with C=500 : 0.7910969793322734

Train SVM with C=1000 

SVM with C=1000 : 0.7926868044515103

Train SVM with C=2000 

SVM with C=2000 : 0.7939586645468999

Train SVM with C=4000 

SVM with C=4000 : 0.7926868044515103

Train SVM with C=10000 

SVM with C=10000 : 0.7930047694753577

Train SVM with C=2000000 

SVM with C=2000000 : 0.7364069952305247 

so now I’m trying to understand what is the difference between LBP and Haralik in features extraction and which is the best to choose?