  SafeAccess is an integrated system designed to provide easier and safer
access to a smart home for people with or without disabilities. The system is
designed to enhance safety and promote the independence of people with
disability (i.e., visually impaired). The key functionality of the system
includes the detection and identification of human and generating contextual
visual summary from the real-time video streams obtained from the cameras
placed in strategic locations around the house. In addition, the system
classifies human into groups (i.e. friends/families/caregiver versus
intruders/burglars/unknown). These features allow the user to grant/deny remote
access to the premises or ability to call emergency services. In this paper, we
focus on designing a prototype system for the smart home and building a robust
recognition engine that meets the system criteria and addresses speed,
accuracy, deployment and environmental challenges under a wide variety of
practical and real-life situations. To interact with the system, we implemented
a dialog enabled interface to create a personalized profile using face images
or video of friend/families/caregiver. To improve computational efficiency, we
apply change detection to filter out frames and use Faster-RCNN to detect the
human presence and extract faces using Multitask Cascaded Convolutional
Networks (MTCNN). Subsequently, we apply LBP/FaceNet to identify a person and
groups by matching extracted faces with the profile. SafeAccess sends a visual
summary to the users with an MMS containing a person's name if any match found
or as "Unknown", scene image, facial description, and contextual information.
SafeAccess identifies friends/families/caregiver versus intruders/unknown with
an average F-score 0.97 and generates a visual summary from 10 classes with an
average accuracy of 98.01%.
