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About A Comprehensive Analysis of Deep Learning Based Representation For Face Recognition
This document presents a comprehensive analysis of using deep learning models to extract representations for face recognition. It evaluates how well representations from two pretrained deep learning models (VGG-Face and Lightened CNN) can handle variations in pose, illumination, occlusion, and misalignment. The results show that while deep learning provides a powerful representation, its performance decreases under large pose, illumination, and occlusion variations, especially if those variations were not present in the training data. Preprocessing, such as pose and illumination normalization, can help deep learning achieve better performance under various conditions.
Detailed Information
| Author: | ['Rajat Rasal'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 8 |
| Language: | English |
| Format: | |
| Price: | FREE |
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