Open AccessEvaluation of Local Descriptors and Deep CNN Features for Face Anti Spoofing
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1121.0882s819
Subject(s) - artificial intelligence , local binary patterns , computer science , histogram of oriented gradients , pattern recognition (psychology) , support vector machine , spoofing attack , histogram , scale invariant feature transform , face (sociological concept) , feature (linguistics) , facial recognition system , deep learning , feature extraction , artificial neural network , computer vision , image (mathematics) , computer network , social science , linguistics , philosophy , sociology
Recently facial recognition Technology are being habitual for various access control requirements and spoof detection in such a system has drawn growing attention. In this paper, we represent by comparison analysis of different local descriptors and off the shelf deep networks for feature extractionLocal Binary Pattern (LBP), SIFT, Histogram of Oriented Gradients (HOG), Shallow CNN, VGG16 and Inception-ResnetV2 for face spoofing detection. Furthermore, we evaluated three Classifiers-Decision Tree, Artificial Neural Network (ANN) and Support Vector Machine (SVM) over the feature extracted through local descriptors and deep networks. The evaluation has been conducted using publicly available YALE face database containing real and fake facial images. Real dataset consists of 5121 entries and fake dataset has 7508 images. The analysis results demonstrate that the best prediction accuracy of real and spoof is obtained with Inception_ResnetV2 features when classified with ANN and about 96.23% accuracy is achieved