Proceedings ArticleDOI
Kernel group sparse representation based classifier for multimodal biometrics
Gaurav Goswami,Richa Singh,Mayank Vatsa,Angshul Majumdar +3 more
- pp 2894-2901
TLDR
A kernelization based extension to the group sparse representation classifier which can utilize multiple representations of input data to improve classification performance and evaluate the proposed algorithm on three challenging biometric problems namely, cross distance face recognition, RGB-D face recognition and multimodal biometrics to showcase its efficacy.Abstract:
Classification is an important pattern recognition paradigm with a multitude of applications in popular research problems. Utilizing multiple data representations to improve the accuracy of classification has been explored in literature. However, approaches such as combining classifiers using majority voting and score level fusion do not utilize the underlying structure of the data which is available at the representation stage itself. In this paper, we propose a kernelization based extension to the group sparse representation classifier which can utilize multiple representations of input data to improve classification performance. By using a kernel, these representations are processed in a higher dimensional space where they are more separable, without substantially increasing computational costs. The proposed algorithm selects the ideal kernel to use along with its parameters automatically as part of the training process. We evaluate the proposed algorithm on three challenging biometric problems namely, cross distance face recognition, RGB-D face recognition, and multimodal biometrics to showcase its efficacy. Experimentally, we observe that the proposed algorithm can efficiently combine multiple data representations to further improve classification performance.read more
Citations
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Implementation of multimodal biometric recognition via multi-feature deep learning networks and feature fusion
TL;DR: The proposed MDLN architecture is designed as a feature-level fusion approach that correlates between the multimodal biometrics data and texture descriptor, which creates a new feature representation to improve recognition performance.
Proceedings ArticleDOI
Rgb-D Based Multi-Modal Deep Learning for Face Identification
TL;DR: This work proposes a multi-modal learning method that achieves 99.7% classification accuracy and exceeds the most state-of-the-art methods, and the global descriptors of model output are designed to be binarized.
Journal ArticleDOI
Dental hard tissue morphological segmentation with sparse representation-based classifier
Bin Cheng,Wei Wang +1 more
TL;DR: A novel algorithm was presented by using the sparse representation-based classifier and mathematical morphology operations to improve the precision of dental hard tissue segmentation and has better adaptability and robustness than existing state-of-the-art methods.
Journal ArticleDOI
Linear Representation-Based Methods for Image Classification: A Survey
TL;DR: The purpose of this paper is to provide a categorization and a comprehensive survey of the existing linear representation-based classification methods for image classification, and Summarize the main applications of the linear represented-based methods.
Dissertation
Unraveling representations for face recognition : from handcrafted to deep learning
TL;DR: This dissertation proposes novel feature extraction and fusion paradigms along with improvements to existing methodologies in order to address the challenge of unconstrained face recognition and presents a novel methodology to improve the robustness of such algorithms in a generalizable manner.
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