Pradipta Kumar Banerjee
@futureeducation.in
Proffesor and Head, Department of CSE-(Artificial Intelligence and Machine LEarning)
FUTURE INSTITUTE OF TECHNOLOGY
EDUCATION
Ph.D., M.tech, B.Tech, B.Sc. (Physics Hons.)
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing
25
Scopus Publications
Scopus Publications
- Phase-Encoded Cascaded Autoencoders based Correlation Filters and Adaptive Thresholding for Illumination Invariant Face Recognition
Pradipta K. Banerjee
Springer Nature Switzerland - Facial emotion recognition using unconstrained minimum average correlation energy filter
Debosmita Chakraborty and Pradipta Kr Banerjee
AIP Publishing - Linear Regression Correlation Filter: An Application to Face Recognition
Tiash Ghosh and Pradipta K. Banerjee
Springer Singapore - Random forest based fault classification technique for active power system networks
Debosmita Chakraborty, Ujjal Sur, and Pradipta Kumar Banerjee
IEEE
In recent times, integration of distributed energy resources with conventional power networks has been increased rapidly and with that several interlinking converters and power electronic devices are there. This increases the complexity of the system. In this paper, a fault classification technique based on random forest classifier has been proposed. As the random forest tree is an artificial intelligence tool, therefore, it is guaranteed the results obtained are of high accuracy value. The high accuracy in fault detection and classification is highly needed for a power system network to eradicate the fault from the system. This method has been tested over both transmission and distribution networks to show the efficacy of this proposed method, where the distribution network is a modified practical Indian distribution grid. Also a comparative study of this method with existing classification techniques like SVM, KNN and others has been done. - A Class Specific Representation Learning for Illumination Tolerant Face Recognition
Tiash Ghosh and Pradipta K. Banerjee
Springer Singapore - Unconstrained band-pass optimized correlation filter (UBoCF): An application to face recognition
Amith Achuthanunni, Ratul Kishore Saha, and Pradipta K. Banerjee
IEEE - Band-pass correlation filter for illumination- and noise-tolerant face recognition
Pradipta K. Banerjee and Asit K. Datta
Springer Science and Business Media LLC - Face detection and recognition: Theory and practice
Asit Kumar Datta, Madhura Datta, and Pradipta Kumar Banerjee
Chapman and Hall/CRC - Class specific subspace dependent nonlinear correlation filtering for illumination tolerant face recognition
Pradipta K. Banerjee and Asit K. Datta
Elsevier BV - Efficient image plane rotation invariant frequency domain face recognition technique using eye localization
Papia Banerjee, Pradipta K. Banerjee, and Asit K. Datta
Springer Berlin Heidelberg - An illumination tolerant class specific 2D subspace based face recognition technique using optimum correlation filter
Pradipta K. Banerjee and Asit K. Datta
Elsevier BV - A preferential digital-optical correlator optimized by particle swarm technique for multi-class face recognition
Pradipta K. Banerjee and Asit K. Datta
Elsevier BV - Generalized regression neural network trained preprocessing of frequency domain correlation filter for improved face recognition and its optical implementation
Pradipta K. Banerjee and Asit K. Datta
Elsevier BV - Techniques of frequency domain correlation for face recognition and its photonic implementation
- Phase-eigen subspace based illumination invariant face recognition using associative memory
Papia Banerjee and Pradipta K. Banerjee
IEEE
Phase eigen subspace based face recognition under varying lighting conditions is proposed. Universal subspace analysis is exploited in frequency domain and phase spectrum is extracted instead of using raw spatial data of face images. Improved results are obtained when simplified bi-directional associative memory neural network is used as classifier. The proposed scheme is experimented over two standard databases like YaleB and PIE and the promising recognition accuracy is achieved while comparing to standard subspace methods. - Sub image based eigen fabrics method using multi-class SVM classifier for the detection and classification of defects in woven fabric
A. Basu, J. K. Chandra, P. K. Banerjee, S. Bhattacharyya, and A. K. Datta
IEEE - Class dependent 2D correlation filter for illumination tolerant face recognition
Pradipta K. Banerjee, Jayanta K. Chandra, and Asit K. Datta
Springer Berlin Heidelberg - Singular value decomposition method for the detection of defects in woven fabric refined by morphological operation
Jayanta K. Chandra, Pradipta K. Banerjee, and Asit K. Datta
IEEE
In this paper a new approach for the detection of defects in woven fabric is presented where the singular value decomposition (SVD) method is used. SVD basically removes the interlaced grating structure of the waft and warp of the fabric leaving aside the defective part of the fabric. An intensity threshold value along with the module of definite size is considered for the binarization of the background free fabric image. Finally, for the removal of the noise from the binary fabric image the morphological opening operation with the suitable structuring element is performed. The technique is tested on 287 fabric samples consisting of five different types of defects in three types of woven fabrics from TILDA database. 94.08% success rate of detection of defects is achieved. - Face detection and eye localization in video by 3D unconstrained filter and neural network
Pradipta K. Banerjee, Jayanta K. Chandra, and Asit K. Datta
Springer Berlin Heidelberg - Feature based optimal trade-off parameter selection of frequency domain correlation filter for real time face authentication
Pradipta K. Banerjee, Jayanta K. Chandra, and Asit K. Datta
ACM Press - Neural network trained morphological processing for the detection of defects in woven fabric
Jayanta K. Chandra, Pradipta K. Banerjee, and Asit K. Datta
Informa UK Limited
Basic morphological operations such as the erosion, dilation, opening, and closing often fail to detect various types of defects that may be present in woven fabric, mainly because of the heuristic selection of structuring element needed for these operations. In this paper, an artificial neural network (ANN) is utilized for the selection of structuring element, where ANN is trained by two pre‐assigned normalized numbers related to the warp and weft counts of the test fabric. The test gray fabric image is pre‐processed to remove noise and the interlaced grating structure of weft and warp and then converted to a binary image by thresholding. An intensity threshold value of the processed fabric image and the dimension of a sliding window needed for correlation operation are obtained from the trained ANN. Defects are detected after morphological reconstruction of the processed binary fabric image, where an ANN trained structuring element is used. The technique is tested on 317 samples for eight different types of defects in three types of plain woven fabrics from TILDA database and 92.8% success of detection is achieved. - A frequency domain face recognition technique based on correlation plane features as input to a regression neural network
Pradipta K. Banerjee, Jayanta K. Cahndra, and Asit K. Datta
Elsevier BV - SVM classifier for face recognition based on unconstrained correlation filter
Pradipta K. Banerjee, Jayanta K. Chandra, and Asit K. Datta
IEEE - Illumination and noise tolerant face recognition based on eigen-phase correlation filter modified by Mexican hat wavelet
Pradipta K. Banerjee and Asit K. Datta
Springer Science and Business Media LLC - Morphological reconstruction operation for the detection of defects in woven fabric
Jayanta K. Chandra, Pradipta K. Banerjee, and Asit K. Datta
IEEE