Title:

Design and Analysis of Facial Recognition Algorithms for Home Monitoring

Facial recognition "in the wild" has historically been a challenge in the field of computer vision. Though facial recognition algorithms are generally proficient at recognizing faces up close, subjects at awkward angles and greater distances from the camera make monitoring areas with this software a practical challenge. At UNH's Cognitive Assistive Robotics Lab (CARL), overcoming the weak areas of face recognition is essential to the task of home monitoring. The CARL research team is implementing a suite of robotics and computer vision technology to monitor patients with Alzheimer's Dementia in their homes. This necessitates a reliable and effective facial recognition pipeline capable of distinguishing subjects of interest. My thesis project begins by establishing a framework for evaluating and comparing the performance of such facial recognition pipelines. Furthermore, it explores potential solutions to the shortcomings of existing facial recognition strategies by introducing custom algorithms centered around footage transformations and enhanced face datasets.

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