Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition

TL;DR

This paper proposes a hierarchical deep CNN feature set-based approach for robust cross-resolution face recognition, effectively utilizing multi-level features and hierarchical fusion to improve accuracy in noisy and resolution-discrepant scenarios.

Contribution

It introduces a novel feature set-based representation learning scheme that adaptively fuses multi-level CNN features and fuses hierarchical recognition outputs for enhanced CRFR performance.

Findings

Outperforms existing CRFR methods on multiple datasets.

Effectively exploits multi-level features for robustness.

Demonstrates improved accuracy in noisy and low-resolution conditions.

Abstract

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little works consider how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches.