Proceedings ArticleDOI
Effect of illicit drug abuse on face recognition
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TLDR
The experimental results show the decreased performance of current face recognition algorithms on drug abuse face images, and a proposed projective Dictionary learning based illicit Drug Abuse face Classification (DDAC) framework to effectively detect and separate faces affected by drug abuse from normal faces is proposed.Abstract:
Over the years, significant research has been undertaken to improve the performance of face recognition in the presence of covariates such as variations in pose, illumination, expressions, aging, and use of disguises. This paper highlights the effect of illicit drug abuse on facial features. An Illicit Drug Abuse Face (IDAF) database of 105 subjects has been created to study the performance on two commercial face recognition systems and popular face recognition algorithms. The experimental results show the decreased performance of current face recognition algorithms on drug abuse face images. This paper also proposes projective Dictionary learning based illicit Drug Abuse face Classification (DDAC) framework to effectively detect and separate faces affected by drug abuse from normal faces. This important pre-processing step stimulates researchers to develop a new class of face recognition algorithms specifically designed to improve the face recognition performance on faces affected by drug abuse. The highest classification accuracy of 88.81% is observed to detect such faces by the proposed DDAC framework on a combined database of illicit drug abuse and regular faces.read more
Citations
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Journal ArticleDOI
Longitudinal Study of Automatic Face Recognition
Lacey Best-Rowden,Anil K. Jain +1 more
TL;DR: Longitudinal analysis of two mugshot databases shows that despite decreasing genuine scores, 99% of subjects can still be recognized at 0.01% FAR up to approximately 6 years elapsed time, and that age, sex, race, and race only marginally influence these trends.
Journal ArticleDOI
Weakly Paired Multimodal Fusion for Object Recognition
TL;DR: A novel projective dictionary learning framework for weakly paired multimodal data fusion is established by introducing a latent pairing matrix, which realizes the simultaneous dictionary learning and the pairing matrix estimation, and therefore improves the fusion effect.
Proceedings ArticleDOI
A longitudinal study of automatic face recognition
Lacey Best-Rowden,Anil K. Jain +1 more
TL;DR: Longitudinal analysis of the scores from the more accurate COTS matcher shows that despite decreasing genuine scores over time, the average subject can still be correctly verified at a false accept rate (FAR) of 0.01% across all 16 years of elapsed time in the database.
Proceedings ArticleDOI
Face recognition using scattering wavelet under Illicit Drug Abuse variations
TL;DR: An autoencoder-style mapping function (AutoScat) is proposed that learns to encode the ScatNet representation of a face image to reduce the computation time.
References
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