├── README.md ├── svmClassification.py ├── lbpExtraction.py └── LICENSE /README.md: -------------------------------------------------------------------------------- 1 | # Simple-FaceAntispoofing 2 | A simple realization of face antispoofing using LBP features and SVM. 3 | 4 | 5 | ## Reference 6 | [1] Zinelabidine Boulkenafet, Jukka Komulainen, Abdenour Hadid. Face Spoofing Detection Using Colour Texture Analysis. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2016 7 | -------------------------------------------------------------------------------- /svmClassification.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import json 3 | from sklearn import model_selection as mo 4 | from sklearn import svm 5 | from sklearn.metrics import classification_report 6 | import matplotlib.pyplot as plt 7 | import skimage 8 | 9 | file = "data.json" 10 | 11 | 12 | def spoof_type(s): 13 | spoof = {'ClientRawface': 0, 'ImposterRawface': 1} 14 | return spoof[s] 15 | 16 | 17 | x_train = [] 18 | x_test = [] 19 | y_train = [] 20 | y_test = [] 21 | y0 = np.array([0]) 22 | y1 = np.array([1]) 23 | with open(file, 'r')as f: 24 | list = json.load(f) 25 | data = np.array(list) 26 | for i in range(len(data)): 27 | # print(data[i]) 28 | x, _ = (np.split(data[i], (1536,), axis=0)) 29 | 30 | if data[i][-1] == 'train_all/real': 31 | x_train.append(x) 32 | y_train.append(0) 33 | if data[i][-1] == 'test_all/real': 34 | x_test.append(x) 35 | y_test.append(0) 36 | if data[i][-1] == 'train_all/attack': 37 | x_train.append(x) 38 | y_train.append(1) 39 | if data[i][-1] == 'test_all/attack': 40 | x_test.append(x) 41 | y_test.append(1) 42 | x_train = np.array((x_train)) 43 | x_test = np.array((x_test)) 44 | y_train = np.array((y_train)) 45 | y_test = np.array((y_test)) 46 | # x_train,x_test,y_train,y_test=mo.train_test_split(x,y,random_state=1,train_size=0.6) 47 | classifier = svm.SVC(C=0.9, kernel='rbf', gamma=30, decision_function_shape='ovo') # C=0.8, gamma=20 48 | classifier.fit(x_train, y_train.ravel()) 49 | print(classifier.score(x_train, y_train)) 50 | 51 | y_hat = classifier.predict(x_train) 52 | print(classification_report(y_train, y_hat, digits=3)) 53 | 54 | y_hat = classifier.predict(x_test) 55 | print(classification_report(y_test, y_hat, digits=3)) 56 | -------------------------------------------------------------------------------- /lbpExtraction.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import os 3 | import numpy as np 4 | from skimage import feature 5 | import json 6 | import glob 7 | import tqdm 8 | 9 | 10 | def get_lbp_hist(channel): 11 | radius = 1 12 | n_point = radius * 8 13 | lbp = feature.local_binary_pattern(channel, n_point, radius, 'ror') 14 | max_bins = int(lbp.max() + 1) 15 | lbp_hist = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins)) 16 | return lbp_hist 17 | 18 | 19 | def make_lbp_data(folder): 20 | root = os.getcwd() 21 | data = [] 22 | imgdirs = glob.glob( 23 | os.path.join(root, folder, '*/*.jpg')) 24 | for file in tqdm.tqdm(imgdirs): 25 | if file.endswith('.jpg'): 26 | img = cv2.imread(file) 27 | hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) 28 | ycbcr = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb) 29 | h, s, v = cv2.split(hsv) 30 | y, cb, cr = cv2.split(ycbcr) 31 | h_hist, s_hist, v_hist = get_lbp_hist(np.array(h)), get_lbp_hist(np.array(s)), get_lbp_hist(np.array(v)) 32 | y_hist, cb_hist, cr_hist = get_lbp_hist(np.array(y)), get_lbp_hist(np.array(cb)), get_lbp_hist(np.array(cr)) 33 | totalhist = np.array([h_hist[0], s_hist[0], v_hist[0], y_hist[0], cb_hist[0], cr_hist[0]]) 34 | totalhist = totalhist.flatten().tolist() 35 | totalhist.append(folder) 36 | data.append(totalhist) 37 | return data 38 | 39 | 40 | def getmulti(): 41 | totaldata = [] 42 | for str in ('train_all/real', 'train_all/attack', 'test_all/real', 'test_all/attack'): 43 | # str = input("Enter the category, input 0 to stop\n") 44 | if str == '0': 45 | break 46 | else: 47 | if not os.path.isdir(str): 48 | print("No file in " + str) 49 | else: 50 | for i in make_lbp_data(str): 51 | totaldata.append(i) 52 | with open('data.json', 'w') as f: 53 | json.dump(totaldata, f) 54 | print('Done') 55 | 56 | 57 | getmulti() 58 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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