Brian C. Parks

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Face recognition in unconstrained environments is one of the most challenging problems in biometrics. One vexing problem in unconstrained environments is that of scale; a face captured at large distances is considerably harder to recognize than the same face at small distances. Several methods have been proposed to tackle unconstrained face recognition in a(More)
This paper is a continuation of the goal to connect power spectra and Deterministic Finite Automata (DFA) in a manner to enhance the detection of spikes and seizures in epileptiform activity from Electroencephalograms (EEG). The goal is to develop robust classification rules for identifying epileptiform activity in the human brain. This paper presents(More)
In the continuing endeavor to create a robust methodology to mine epileptiform activity from Electroencephalograms (EEG) by combining the methodologies of Deterministic Finite Automata (DFA) and Knowledge Discovery in Data Mining (KDD), this paper presents a more efficient model for identifying epileptiform activity and seizures from Electroencephalograms(More)
This paper presents the merging of two sets of experiments in the continuing endeavor to mine epileptiform activity from Electroencephalograms (EEG). The goal is to develop robust classification rules for identifying epileptiform activity in the human brain. We present advancements using the author’s proprietary developed spectral analysis software to link(More)
Generating statistically significant datasets for face matching system evaluation is a laborious and expensive process. Capturing variables such as atmospheric turbulence and other weather conditions especially with respect to face recognition at a distance exacerbate the problem further. It is even more difficult to work on system issues for long-range(More)
Blur due to motion and atmospheric turbulence is a variable that impacts the accuracy of computer vision-based face recognition techniques. However, in images captured in the wild, such variables can hardly be avoided, requiring methods to account for these degradations in order to achieve accurate results in real time. One such method is to estimate the(More)
Visual processing in humans is, without a doubt, far superior that that in machines, especially when the end goal is object or face recognition. Neural results from visual object and face recognition in humans provide an excellent model for developing better techniques in machine vision. In this study, we present a particular neural result pertaining to the(More)
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