On the complex domain deep machine learning for face recognition

Biometric based verification and recognition has become the center of attention for many significant applications for security conscious societies, as it is believed that biometrics can provide accurate and reliable identification. The face biometrics are one that possesses the merits of both high accuracy and low intrusiveness. An efficient machine recognition of human faces in big dataset is both important and challenging tasks. This paper addresses an intelligent face recognition system that is pose invariant and can recognize multi-expression, occluded and blurred faces through efficient but compact deep learning. Superior functionality of neural network in a complex domain has been observed in recent researches. My work presents a new approach, which is the fusion of higher-order novel neuron models with multivariate statistical techniques in a complex domain with a sole goal of improving performance of biometric systems. This also aims at reducing the computational cost and providing a faster recognition system. This paper presents the formal algorithms for feature extraction with multivariate statistical techniques in complex domain and compare them their real domain counterpart. This paper also presents a classifier structure (OCON : One-Class-in-One-Neuron) which contains an ensemble of novel higher order neurons, which drastically reduces the complexity of proposed learning machine because only single neuron is sufficient to recognize a subject in the database. This novel fusion in the proposed deep learning machine has thoroughly presented its superiority over a wide spectrum of experiments. Advanced deep learning capabilities, and complex domain implementation in particular, are significantly advancing state-of-art in computer vision and pattern recognition.

[1]  Erkki Oja,et al.  The FastICA Algorithm Revisited: Convergence Analysis , 2006, IEEE Transactions on Neural Networks.

[2]  Wei Lu,et al.  ICA with Reference , 2006, Neurocomputing.

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

[4]  William W. Hsieh,et al.  Complex-valued neural networks for nonlinear complex principal component analysis , 2005, Neural Networks.

[5]  De-Shuang Huang,et al.  A New Constrained Independent Component Analysis Method , 2007, IEEE Transactions on Neural Networks.

[6]  J. W. Brown,et al.  Complex Variables and Applications , 1985 .

[7]  Danilo P. Mandic,et al.  Complex Valued Nonlinear Adaptive Filters , 2009 .

[8]  Akira Hirose,et al.  Complex-Valued Neural Networks , 2006, Studies in Computational Intelligence.

[9]  K. M. Bhurchandi,et al.  Expression invariant face recognition using semidecimated DWT, Patch-LDSMT, feature and score level fusion , 2015, Applied Intelligence.

[10]  S. Hahn.  Hilbert Transforms in Signal Processing , 1996 .

[11]  T. Adali,et al.  Unmixing fMRI with independent component analysis , 2006, IEEE Engineering in Medicine and Biology Magazine.

[12]  Matti Pietikäinen,et al.  Face recognition based on the appearance of local regions , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  Prem Kumar Kalra,et al.  On Efficient Learning Machine With Root-Power Mean Neuron in Complex Domain , 2011, IEEE Transactions on Neural Networks.

[14]  Terrence J. Sejnowski,et al.  Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data , 2003, Neural Networks.

[15]  Vince D. Calhoun,et al.  Complex Infomax: Convergence and Approximation of Infomax with Complex Nonlinearities , 2006, J. VLSI Signal Process..

[16]  Vince D. Calhoun,et al.  On complex infomax applied to functional MRI data , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  Tülay Adali,et al.  Complex ICA by Negentropy Maximization , 2008, IEEE Transactions on Neural Networks.

[18]  Mita Nasipuri,et al.  Human face recognition using fuzzy multilayer perceptron , 2010, Soft Comput..

[19]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[20]  Gian Luca Marcialis,et al.  Fusion of appearance-based face recognition algorithms , 2004, Pattern Analysis and Applications.

[21]  E. Saff,et al.  Fundamentals of complex analysis with applications to engineering and science , 2003 .

[22]  Meng Joo Er,et al.  High-speed face recognition based on discrete cosine transform and RBF neural networks , 2005, IEEE Transactions on Neural Networks.

[23]  Chein-I Chang,et al.  Robust radial basis function neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[24]  Bartlett W. Mel,et al.  Information Processing in Dendritic Trees , 1994, Neural Computation.

[25]  Bipin Kumar Tripathi,et al.  Hybrid Computation Model for Intelligent System Design by Synergism of Modified EFC with Neural Network , 2015, Int. J. Inf. Technol. Decis. Mak..

[26]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[27]  Sung Bum Pan,et al.  A face recognition system based on convolution neural network using multiple distance face , 2017, Soft Comput..

[28]  Claudio Moraga,et al.  Multilayer Feedforward Neural Network Based on Multi-valued Neurons (MLMVN) and a Backpropagation Learning Algorithm , 2006, Soft Comput..

[29]  Hakan Cevikalp,et al.  Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Kazuyuki Murase,et al.  Single-layered complex-valued neural networks and their ensembles for real-valued classification problems , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[31]  Karim Faez,et al.  Neural network based face recognition with moment invariants , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[32]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[33]  Witold Pedrycz,et al.  Face Recognition Using an Enhanced Independent Component Analysis Approach , 2007, IEEE Transactions on Neural Networks.

[34]  Visa Koivunen,et al.  Complex random vectors and ICA models: identifiability, uniqueness, and separability , 2005, IEEE Transactions on Information Theory.

[35]  Andrea F. Abate,et al.  2D and 3D face recognition: A survey , 2007, Pattern Recognit. Lett..

[36]  Mahantapas Kundu,et al.  Face recognition using point symmetry distance-based RBF network , 2007, Appl. Soft Comput..

[37]  Tohru Nitta,et al.  An Extension of the Back-Propagation Algorithm to Complex Numbers , 1997, Neural Networks.

[38]  Kazuyuki Murase,et al.  Single-layered complex-valued neural network for real-valued classification problems , 2009, Neurocomputing.

[39]  Prem Kumar Kalra,et al.  On the learning machine for three dimensional mapping , 2011, Neural Computing and Applications.

[40]  Tai-hoon Kim,et al.  Face Recognition Using Neural Network: A Review , 2016 .

[41]  Prem Kumar Kalra,et al.  Complex generalized-mean neuron model and its applications , 2011, Appl. Soft Comput..

[42]  Prem Kumar Kalra,et al.  The Generalized Product Neuron Model in Complex Domain , 2008, ICONIP.

[43]  D. Mandic,et al.  Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models , 2009 .

[44]  Matt Aitkenhead,et al.  A neural network face recognition system , 2003 .

[45]  Anand Tiwari,et al.  Neurological Disorder Identification by Eye Movement Biometric using Machine Learning Schemes , 2015 .