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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition.

Jiwen Lu, Gang Wang, Jie Zhou

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 15, 2017

Summary

This summary is machine-generated.

We introduce a simultaneous feature and dictionary learning (SFDL) method for robust face recognition from image sets. This approach jointly learns features and dictionaries, outperforming sequential methods on diverse datasets.

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Area of Science:

Computer Vision
Machine Learning
Pattern Recognition

Background:

Image set-based face recognition faces challenges due to variations in pose, illumination, expression, resolution, and motion.
Existing methods often learn features and dictionaries sequentially, potentially losing discriminative information.

Purpose of the Study:

To propose a Simultaneous Feature and Dictionary Learning (SFDL) method for enhanced image set-based face recognition.
To address the limitations of sequential learning by jointly exploiting discriminative information.

Main Methods:

Developed a SFDL method to learn discriminative features and dictionaries simultaneously from raw face pixels.
Introduced a deep SFDL (D-SFDL) method incorporating hierarchical non-linear transformations and class-specific dictionaries to capture complex variations.

Main Results:

SFDL and D-SFDL methods were evaluated on five widely used face datasets.
Both proposed methods demonstrated highly competitive and superior performance compared to state-of-the-art techniques.

Conclusions:

Simultaneous learning of features and dictionaries significantly improves face recognition accuracy from image sets.
The D-SFDL method effectively handles non-linear variations in face samples, offering enhanced recognition capabilities.