  This research presents an improved real-time face recognition system at a low
resolution of 15 pixels with pose and emotion and resolution variations. We
have designed our datasets named LRD200 and LRD100, which have been used for
training and classification. The face detection part uses the Viola-Jones
algorithm, and the face recognition part receives the face image from the face
detection part to process it using the Local Binary Pattern Histogram (LBPH)
algorithm with preprocessing using contrast limited adaptive histogram
equalization (CLAHE) and face alignment. The face database in this system can
be updated via our custom-built standalone android app and automatic restarting
of the training and recognition process with an updated database. Using our
proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px
and 98.05% at 45 px have been achieved using the LRD200 database containing 200
images per person. With 100 images per person in the database (LRD100) the
achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial
deflection of about 30 degrees on either side from the front face showed an
average face recognition precision of 72.25% - 81.85%. This face recognition
system can be employed for law enforcement purposes, where the surveillance
camera captures a low-resolution image because of the distance of a person from
the camera. It can also be used as a surveillance system in airports, bus
stations, etc., to reduce the risk of possible criminal threats.
