Cancellable Face Biometrics Template Using AlexNet

Biometric systems with traits like gesture, voice, fingerprint, palm print, handwritten signature, hand geometry, face, and iris have been utilized for authentication. Through these traits, face trait is considered as one of the strongest and an important biometric element. In this work, a presentation of a new cancellable algorithm of face image which is dependent on AlexNet and Winner-Takes-All (WTA) hash method has been proposed. AlexNet is a Convolutional Neural Networks (CNNs) that reached a state-of-the-art level of recognition precision compared to other conventional machine learning methods in terms of feature execution. WTA is used for similarity purposes, whereas random binary orthogonal matrices are applied to produce the projected features of vectors. Fundacao Educacional Inaciana dataset and Georgia tech face dataset were used in evaluating the performance of the proposed algorithm. Experimental results illustrate the proposed algorithm has satisfactory execution performance in terms of Equal Error Rate. Thus the proposed algorithm can be used as an alternative method in security biometric implementation.

[1]  Bhabatosh Chanda,et al.  A novel cancelable iris recognition system based on feature learning techniques , 2017, Inf. Sci..

[2]  Chris Aldrich,et al.  Froth image analysis by use of transfer learning and convolutional neural networks , 2018 .

[3]  Ali Ismail Awad,et al.  From classical methods to animal biometrics: A review on cattle identification and tracking , 2016, Comput. Electron. Agric..

[4]  Ming Li,et al.  Cancellable speech template via random binary orthogonal matrices projection hashing , 2018, Pattern Recognit..

[5]  Joris IJsselmuiden,et al.  Transfer learning for the classification of sugar beet and volunteer potato under field conditions , 2018, Biosystems Engineering.

[6]  Jay Yagnik,et al.  The power of comparative reasoning , 2011, 2011 International Conference on Computer Vision.

[7]  Sridha Sridharan,et al.  Super-resolution for biometrics: A comprehensive survey , 2018, Pattern Recognit..

[8]  G. Radhamani,et al.  New chaff point based fuzzy vault for multimodal biometric cryptosystem using particle swarm optimization , 2016, J. King Saud Univ. Comput. Inf. Sci..

[9]  Vijayan K. Asari,et al.  The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches , 2018, ArXiv.

[10]  Baptiste Hemery,et al.  Performance Evaluation of Behavioral Biometric Systems , 2010 .

[11]  Neyire Deniz Sarier.  Multimodal biometric Identity Based Encryption , 2018, Future Gener. Comput. Syst..

[12]  Andrew Beng Jin Teoh,et al.  Kernel PCA enabled bit-string representation for minutiae-based cancellable fingerprint template , 2016, Pattern Recognit..

[13]  Zhe Jin,et al.  Biometric cryptosystems: A new biometric key binding and its implementation for fingerprint minutiae-based representation , 2016, Pattern Recognit..

[14]  Craig Valli,et al.  Impact of feature proportion on matching performance of multi-biometric systems , 2019, ICT Express.

[15]  Pritee Khanna,et al.  Gaussian Random Projection Based Non-invertible Cancelable Biometric Templates , 2015 .

[16]  Nalini K. Ratha,et al.  Generating Cancelable Fingerprint Templates , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Jiankun Hu,et al.  Design of Alignment-Free Cancelable Fingerprint Templates with Zoned Minutia Pairs , 2017, Pattern Recognit..

[18]  Yen-Lung Lai,et al.  Cancellable iris template generation based on Indexing-First-One hashing , 2017, Pattern Recognit..

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Muhammad Ali Akbar,et al.  Secure biometric template generation for multi-factor authentication , 2015, Pattern Recognit..

[21]  Kenta Takahashi,et al.  Optimal sequential fusion for multibiometric cryptosystems , 2016, Inf. Fusion.

[22]  M. Shamim Hossain,et al.  Edge-centric multimodal authentication system using encrypted biometric templates , 2018, Future Gener. Comput. Syst..

[23]  Arun Kumar Sangaiah,et al.  Lip biometric template security framework using spatial steganography , 2019, Pattern Recognit. Lett..

[24]  M. A. H. Akhand,et al.  Noisy image classification using hybrid deep learning methods , 2018 .

[25]  Ling Huang,et al.  Optimization of deep convolutional neural network for large scale image retrieval , 2018, Neurocomputing.

[26]  Singh Chalga Manjeet,et al.  Cancellable Biometrics for Security and Privacy Enforcement on Semantic Web , 2011 .

[27]  Aditya Prasad,et al.  A privacy-preserving cancelable iris template generation scheme using decimal encoding and look-up table mapping , 2017, Comput. Secur..