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Image Generation Using Bidirectional Integral Features for Face Recognition with a Single Sample per Person.

Yonggeol Lee¹, Minsik Lee², Sang-Il Choi¹

¹Department of Computer Science and Engineering, Dankook University, 126, Jukjeon-dong, Suji-gu, Yongin-si, Gyeonggi-do, 448-701, Korea.

|September 29, 2015

Summary

This summary is machine-generated.

This study introduces a new method for face recognition using only one image per person. It generates varied illumination images from a single sample, improving recognition accuracy in real-world scenarios.

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Area of Science:

Computer Vision
Biometrics
Machine Learning

Background:

Appearance-based face recognition typically requires multiple images per individual.
Real-world applications often face limitations, resulting in single sample per person (SSPP) datasets.
Illumination variation is a significant challenge in face recognition systems.

Purpose of the Study:

To develop a novel method for generating diverse facial images from a single input.
To enhance face recognition performance under challenging illumination conditions.
To address the limitations of SSPP datasets in biometric identification.

Main Methods:

Proposes a technique to synthesize new images with varying illuminations from a single frontal image.
Utilizes the concept of integral images to extract bidirectional integral features (BIF).
BIF captures illumination characteristics present in the original image.

Main Results:

Experimental validation on multiple face databases demonstrates improved recognition accuracy.
The method effectively handles variations in lighting conditions.
Successful generation of varied illumination images from single samples.

Conclusions:

The proposed method offers a viable solution for face recognition with SSPP data.
Bidirectional integral features (BIF) are effective in characterizing illumination for improved recognition.
This approach enhances the robustness of face recognition systems against illumination changes.