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Face recognition systems are susceptible to presentation attacks such as printed photo attacks, replay attacks, and 3D mask attacks. These attacks, primarily studied in visible spectrum, aim to obfuscate or impersonate a person's identity. This paper presents a unique multispectral video face database for face presentation attack using latex and paper masks. The proposed Multispectral Latex Mask based...
This paper focuses on decoding the process of face verification in the human brain using fMRI responses. 2400 fMRI responses are collected from different participants while they perform face verification on genuine and imposter stimuli face pairs. The first part of the paper analyzes the responses covering both cognitive and fMRI neuro-imaging results. With an average verification accuracy of 64.79%...
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...
Researchers have shown that the changes in face features due to plastic surgery can be modeled as a covariate that reduces the ability of algorithms to recognize a person’s identity. Traditional dictionary learning methods learn a sparse representation using $l_{0}$ and $l_{1}$ norms that are computationally expensive. This paper presents a multiple projective dictionary learning (MPDL) framework...
The phenomenon of "aging" in humans leads to significant variations in facial features. Various factors such as bone growth, ethnicity and dietary habits influence the facial aging pattern. This increases the difficulty in performing automated face recognition. In this paper, we propose an algorithm that improves the performance of face recognition by applying the bacteria foraging fusion...
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