Cross domain association using transfer subspace learning

Transfer learning has gained more attention recently by utilizing knowledge acquired from one domain to advance a learning performance in another domain. Existing homogeneous transfer learning methods have progressed to a point where feature spaces are common in training and testing domains. However, heterogeneous transfer learning is still in its nascent stage where features of training and testing domains are different. Taking this into account, Bregman Divergence Regularization is used to minimize the probability distribution difference between training and testing domains and to take them together to a shared subspace. To discriminate data within individual domains, a projection matrix is obtained using Fisher Linear Discriminant Analysis subspace learning algorithm. Experimentation is performed on two efficiently used biometrics: the face and fingerprint. Two types of cross-domain settings are used: (1) Face + Finger2Finger where training samples come from face (labeled samples) and fingerprint (unlabeled samples) data sets, while testing is performed on a fingerprint dataset. (2) Finger + Face2Face where training samples come from fingerprint (labeled samples) and face (unlabeled samples) data sets while testing is performed on a face dataset. This paper proposes a cross domain association between face and fingerprint that finds utility in forensic applications.

[1]  Bianca Zadrozny,et al.  Learning and evaluating classifiers under sample selection bias , 2004, ICML.

[2]  Qiang Yang,et al.  A unified framework of active transfer learning for cross-system recommendation , 2017, Artif. Intell..

[3]  Marcelo H. Ang,et al.  Cross-dataset facial expression recognition , 2011, 2011 IEEE International Conference on Robotics and Automation.

[4]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[5]  Seokho Kang,et al.  On Effectiveness of Transfer Learning Approach for Neural Network-Based Virtual Metrology Modeling , 2018, IEEE Transactions on Semiconductor Manufacturing.

[6]  Klemen Grm,et al.  Strengths and weaknesses of deep learning models for face recognition against image degradations , 2017, IET Biom..

[7]  Ali Farhadi,et al.  Transfer Learning in Sign language , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Dacheng Tao,et al.  Evolutionary Cross-Domain Discriminative Hessian Eigenmaps , 2010, IEEE Transactions on Image Processing.

[9]  Meng Wang,et al.  Social image annotation via cross-domain subspace learning , 2010, Multimedia Tools and Applications.

[10]  Reuben A. Farrugia,et al.  Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning , 2018, IEEE Transactions on Information Forensics and Security.

[11]  Y. L. Yu,et al.  Face recognition with eigenfaces , 1994, Proceedings of 1994 IEEE International Conference on Industrial Technology - ICIT '94.

[12]  M. Sharkas,et al.  Eigenfaces vs. fisherfaces vs. ICA for face recognition; a comparative study , 2008, 2008 9th International Conference on Signal Processing.

[13]  Mallikarjun S. Holi,et al.  Face recognition using local features by LPP approach , 2014, International Conference on Circuits, Communication, Control and Computing.

[14]  Qi Tian,et al.  Cross-database age estimation based on transfer learning , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Eunjeong Park,et al.  A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer , 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom).

[16]  Mei Zhou,et al.  Bimodal Vein Data Mining via Cross-Selected-Domain Knowledge Transfer , 2018, IEEE Transactions on Information Forensics and Security.

[17]  Tze-Yun Leong,et al.  Scalable transfer learning in heterogeneous, dynamic environments , 2017, Artif. Intell..

[18]  Jiawei Han,et al.  Learning a Maximum Margin Subspace for Image Retrieval , 2008, IEEE Transactions on Knowledge and Data Engineering.

[19]  Rich Caruana,et al.  Multitask Learning , 1997, Machine Learning.

[20]  Vibha Vyas,et al.  Component-based face recognition under transfer learning for forensic applications , 2019, Inf. Sci..

[21]  Caikou Chen,et al.  Enhanced Marginal Fisher Analysis for Face Recognition , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[22]  Uwe Aickelin,et al.  Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms , 2009, Evol. Intell..

[23]  Dacheng Tao,et al.  Discriminative Locality Alignment , 2008, ECCV.

[24]  Jiebo Luo,et al.  Understanding Kin Relationships in a Photo , 2012, IEEE Transactions on Multimedia.

[25]  Jaume Bacardit,et al.  A learning classifier system with mutual-information-based fitness , 2007, 2007 IEEE Congress on Evolutionary Computation.

[26]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[27]  Bin Li,et al.  Online Transfer Learning , 2014, Artif. Intell..

[28]  Yew-Soon Ong,et al.  Towards a new Praxis in optinformatics targeting knowledge re-use in evolutionary computation: simultaneous problem learning and optimization , 2016, Evolutionary Intelligence.

[29]  E. Walia.  Face Recognition Using Improved Fast PCA Algorithm , 2008, 2008 Congress on Image and Signal Processing.

[30]  Qiang Yang,et al.  Transfer Learning via Dimensionality Reduction , 2008, AAAI.

[31]  Vibha Vyas,et al.  A novel training algorithm for convolutional neural network , 2016, Complex & Intelligent Systems.

[32]  Jiawei Han,et al.  Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[33]  Marcia Hon,et al.  Towards Alzheimer's disease classification through transfer learning , 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[34]  Dacheng Tao,et al.  Bregman Divergence-Based Regularization for Transfer Subspace Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[35]  John Blitzer,et al.  Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.

[36]  Seda Kul,et al.  Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification , 2018, Evolutionary Intelligence.

[37]  V. Vyas,et al.  Biometric association using transfer subspace learning , 2016, 2016 IEEE Region 10 Conference (TENCON).

[38]  Mumtaz Ahmed,et al.  Identification of diabetic retinopathy in eye images using transfer learning , 2017, 2017 International Conference on Computing, Communication and Automation (ICCCA).

[39]  Kristen Grauman,et al.  Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition , 2014, International Journal of Computer Vision.

[40]  Feng Liu,et al.  Heterogeneous Transfer Learning: An Unsupervised Approach , 2017 .

[41]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[42]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[43]  Koby Crammer,et al.  Learning Bounds for Domain Adaptation , 2007, NIPS.

[44]  Andrew Y. Ng,et al.  Transfer learning for text classification , 2005, NIPS.

[45]  Qiang Yang,et al.  Transfer learning in heterogeneous collaborative filtering domains , 2013, Artif. Intell..

[46]  Ravindra C. Thool,et al.  SURVEY OF BIOMETRIC RECOGNITION SYSTEMS AND THEIR APPLICATIONS , 2010 .