Proceedings ArticleDOI
Quality assessment based denoising to improve face recognition performance
Samarth Bharadwaj,Himanshu Bhatt,Mayank Vatsa,Richa Singh,Afzel Noore +4 more
- pp 140-145
TLDR
In the proposed framework, the optimal parameter set is selected for denoising using quality assessment algorithms with low complexity using quality score based parameter selection on the AR face dataset.Abstract:
A probe face image may contain noise due to environmental conditions, incorrect use of sensors or transmission error. The performance of face recognition severely depletes when the probe image is contaminated with noise. Denoising techniques can improve recognition performance, provided the correct parameters are used. In this paper, a parameter selection framework is presented. In the proposed framework, the optimal parameter set is selected for denoising using quality assessment algorithms with low complexity. Quality score based parameter selection is evaluated on the AR face dataset. A correlation study is discussed to ascertain the relationship between the quality scores and recognition rate. The experiments suggest that the proposed framework improves the performance both in terms of accuracy and computation time.read more
Citations
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Adaptive Regression Splines Models for Predicting Facial Image Verification and Quality Assessment Scores
TL;DR: Adaptive regression splines (ARES) models were built for predicting algorithm matching scores (AMS) and overall quality scores (OQS) and a face verification and image quality assessment (FVIQA) framework was adopted to extract five facial quality features from still images.
Dissertation
Harnessing auxiliary information : New methods to improve person identification
TL;DR: This thesis introduces a novel learning based approach to face recognition towards an affordable and friendly biometric for newborns and shows that association rules extracted from social context can be used to augment face recognition and improve the identification performance.
Journal ArticleDOI
A Novel Approach of Low-Light Image Denoising for Face Recognition
Yimei Kang,Wang Pan +1 more
TL;DR: A very simple and efficient novel low-light image denoising of low frequency noise (DeLFN) is proposed that not only outperformed other algorithms in improving visual quality and face recognition rate, but also is simpler and computationally efficient for real time applications.
Book ChapterDOI
Assessment of Facial Recognition System Performance in Realistic Operating Environments
TL;DR: This work introduces a methodology to explore the sensitivities of a facial recognition imaging system to blur, noise, and turbulence effects and the ramifications of these results on the design of long-range facial recognition systems.
Dissertation
Using the 3D shape of the nose for biometric authentication
TL;DR: The recognition ranks provide the highest identification performance ever reported for the 3D nasal region, and are not only higher than the previous 3D nose recognition algorithms, but also better than or very close to recent results for whole 3D face recognition.
References
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