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On-the-move heterogeneous face recognition in frequency and spatial domain using sparse representation.

Asif Raza Butt¹, Sajjad Manzoor^1,2, Asim Baig³

¹Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur, AJK, Pakistan.

|October 4, 2024

Summary

This summary is machine-generated.

This study introduces spatial sparse representations (SSR) and frequency sparse representation (FSR) for recognizing heterogeneous face images from multiple cameras. These novel methods demonstrate superior performance in challenging surveillance scenarios.

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Area of Science:

Computer Vision
Image Processing
Biometrics

Background:

Surveillance systems face challenges with heterogeneous probe images due to multiple cameras operating in different spectral ranges.
Recognizing faces across diverse visual and infrared (IR) modalities, especially with limited data (single sample per person - SSPP), is a significant hurdle.

Purpose of the Study:

To propose and evaluate two novel approaches, spatial sparse representations (SSR) and frequency sparse representation (FSR), for on-the-move heterogeneous face recognition.
To assess the performance of these methods under various conditions, including varying distances, face image sizes, and different visual/IR modalities.

Main Methods:

Implementation of spatial sparse representations (SSR) and frequency sparse representation (FSR) for heterogeneous face image recognition.
Utilizing a least squares minimization approach for efficient face image matching.
Benchmarking against state-of-the-art methods including PCA, KFA, CKE, and LRPP-GRR using SCface and CASIA NIR-VIS 2.0 databases.

Main Results:

The proposed SSR and FSR methods demonstrated superior performance in recognizing heterogeneous face images.
Effective recognition was achieved even with variations in distance, image size, and across different visual and IR spectral ranges.
The methods proved effective for the challenging single sample per person (SSPP) recognition task.

Conclusions:

Spatial and frequency sparse representations offer a robust solution for on-the-move heterogeneous face recognition in complex surveillance environments.
The proposed methods provide a significant advancement over existing techniques, particularly for multi-modal and SSPP face recognition challenges.