Face recognition using particle swarm optimization based block ICA

Face recognition is one of the most important and widely applicable research problems in the subject area of machine learning and computer vision. Extraction of features, local or holistic, is the fundamental step and subspace method has been a natural choice for facial feature extraction. Among the...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 80; no. 28-29; pp. 35685 - 35695
Main Authors: Pati, Rasmikanta, Pujari, Arun K, Gahan, Padmavati
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2021
Springer Nature B.V
Subjects:
1166: Advances of machine learning in data analytics and visual information processing
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Face recognition
Feature extraction
Image contrast
Machine learning
Maximum likelihood estimation
Multimedia Information Systems
Object recognition
Optimization
Particle swarm optimization
Separation
Special Purpose and Application-Based Systems
Subspace methods
Training
Swarm optimization
Block independent component analysis
Face recognition
Subspace methods
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary:Face recognition is one of the most important and widely applicable research problems in the subject area of machine learning and computer vision. Extraction of features, local or holistic, is the fundamental step and subspace method has been a natural choice for facial feature extraction. Among these, methods like PCA, ICA, LDA aim to reduce the dimension of the data while retaining the statistical separation property between distinct classes. Unlike the traditional ICA, in which the whole face image is stretched into a vector before calculating the independent components (ICs), Block ICA (B-ICA) partitions the facial images into blocks and takes the block as the training vector. Since the dimensionality of the training vector in B-ICA is much smaller than that in traditional ICA, reduction in face recognition error is expected. The objective of ICA is to find a separation matrix and it is achieved by a process of optimization, such as maximization of non-Gaussianity, maximum likelihood estimation, and minimization of mutual information. We observe here that the gradient-based learning can be efficiently and effectively achieved by the application of swarm-based optimization. We propose here the application of our Gradient-based Swarm Optimization method for Block ICA, where gradient information is combined with conventional swarm search to optimize the contrast function. We compare our method with B-ICA on three benchmark image data sets and show that our method achieved a better recognition rate compared to B-ICA in different block sizes with 70 % , 80 % and 90 % data used for training the model.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-10792-5