Evaluation of Local Descriptors and Deep CNN Features for Face Anti Spoofing
Sandan Priya1, Sanjay Pawar2, Akanksha Joshi3
1Sandan Priya,
2Sanjay Pawar,
3Akanksha Joshi,
Manuscript received on 23 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1644-1648 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B11210882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1121.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: LBP, SIFT, HOG, ANN, SVM, CNN, Face Quality.
Scope of the Article: Deep Learning