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

Person Identification with Visual Summary for a Safe Access to a Smart Home