Thermal infrared face recognition based on lattice computing (LC) techniques

This work introduces a novel methodology for human face recognition based on lattice computing kNN classification techniques applied on thermal infrared images. Novel feature extraction and knowledge-representation engage populations of orthogonal moments represented by intervals' numbers, or INs for short. Preliminary experimental results compare well with the results by alternative classifiers as well as with alternative feature extraction techniques from the literature. We point out the far-reaching potential of the proposed techniques to big data applications.

[1]  Athanasios Kehagias,et al.  Fuzzy Inference System (FIS) Extensions Based on the Lattice Theory , 2014, IEEE Transactions on Fuzzy Systems.

[2]  Vassilis G. Kaburlasos,et al.  Person Identification Based on Lattice Computing k-Nearest-Neighbor Fingerprint Classification , 2012, KES.

[3]  George A. Papakostas,et al.  Novel moment invariants for improved classification performance in computer vision applications , 2010, Pattern Recognit..

[4]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  James Zijun Wang,et al.  Feature Selection in AVHRR Ocean Satellite Images by Means of Filter Methods , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Darya Chyzhyk,et al.  Active Learning with Bootstrapped Dendritic Classifier applied to medical image segmentation , 2013, Pattern Recognit. Lett..

[7]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Joseph N. Wilson,et al.  Handbook of computer vision algorithms in image algebra , 1996 .

[9]  Roberto Carrillo,et al.  Recent Advances on Face Recongition using Thermal Infrared Images , 2011 .

[10]  Peter Sussner,et al.  Quantale-based autoassociative memories with an application to the storage of color images , 2013, Pattern Recognit. Lett..

[11]  Vassilis G. Kaburlasos,et al.  Granular self-organizing map (grSOM) for structure identification , 2006, Neural Networks.

[12]  Vassilis G. Kaburlasos,et al.  Binary Image 2D Shape Learning and Recognition Based on Lattice-Computing (LC) Techniques , 2011, Journal of Mathematical Imaging and Vision.

[13]  J. Flusser,et al.  Moments and Moment Invariants in Pattern Recognition , 2009 .

[14]  Manuel Graña,et al.  Lattice independent component analysis for appearance-based mobile robot localization , 2011, Neural Computing and Applications.

[15]  Stefano Bromuri,et al.  Multi-Dimensional Causal Discovery , 2013, IJCAI.

[16]  Bogdan Raducanu,et al.  Pose-Invariant Face Recognition in Videos for Human-Machine Interaction , 2012, ECCV Workshops.

[17]  Dimitris E. Koulouriotis,et al.  Moment-based local binary patterns: A novel descriptor for invariant pattern recognition applications , 2013, Neurocomputing.

[18]  George A. Papakostas,et al.  Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Vassilis G. Kaburlasos,et al.  A Lattice-Computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application , 2014, Inf. Fusion.

[20]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[21]  Vassilis G. Kaburlasos Towards a Unified Modeling and Knowledge-Representation based on Lattice Theory: Computational Intelligence and Soft Computing Applications (Studies in Computational Intelligence) , 2006 .

[22]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[23]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[24]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[25]  Vassilis G. Kaburlasos,et al.  Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals' numbers (INs) , 2010, Inf. Sci..

[26]  Khairul Hamimah Abas,et al.  Multilayer infrared-based face identification system for security vehicle-robot vision , 2009 .

[27]  George A. Papakostas,et al.  Orthogonal Image Moment Invariants: Highly Discriminative Features for Pattern Recognition Applications , 2012 .

[28]  Manuel Graña Lattice computing and natural computing , 2009, Neurocomputing.

[29]  Mita Nasipuri,et al.  A Comparative Study of Human Thermal Face Recognition Based on Haar Wavelet Transform and Local Binary Pattern , 2012, Comput. Intell. Neurosci..