Long Distance Face Recognition for Enhanced Performance of Internet of Things Service Interface

Hae-Min Moon, Sung Bum Pan

As many objects in the human ambient environment are intellectualized and networked, research on IoT technology have increased to improve the quality of human life. This paper suggests an LDA-based long distance face recognition algorithm to enhance the intelligent IoT interface. While the existing face recognition algorithm uses single distance image as training images, the proposed algorithm uses face images at distance extracted from 1m to 5m as training images. In the proposed LDA-based long distance face recognition algorithm, the bilinear interpolation is used to normalize the size of the face image and a Euclidean Distance measure is used for the similarity measure. As a result, the proposed face recognition algorithm is improved in its performance by 6.1% at short distance and 31.0% at long distance, so it is expected to be applicable for USN’s robot and surveillance security systems.