Abstract
In recent years, various studies have been conducted to provide a real-time service based on face recognition in Internet of things environments such as in a smart home environment. In particular, face recognition in a network-based surveillance camera environment can significantly change the performance or utilization of face recognition technology because the size of image information to be transmitted varies depending on the communication capabilities. In this paper, we propose a multiresolution face recognition method that uses virtual facial images by distance as learning to solve the problem of low recognition rate caused by communication, camera, and distance change. Face images for each virtual distance are generated through clarity and image degradation for each resolution, using a single high-resolution face image. The proposed method achieved a performance that was 5.9% more accurate than methods using MPCA and SVM, when LDA and the Euclidean distance were employed for a DB that was configured using faces that were acquired from the real environments of five different streets.
Highlights
IoT is an intelligent system that helps communicate with people and things or between things and things using Internet networks
When the standard image size was set to the average size of all face images at 30×30, under the conditions using LDA and the Euclidean distance, the average performance was improved by 40.7% for 16×16, 12×12, and 10×10, which are low-resolutions
There is a difference between real lowresolution face images that are extracted from long distances and low-resolution face images that have been reduced to the same size using a high-resolution image
Summary
IoT is an intelligent system that helps communicate with people and things or between things and things using Internet networks. Even if high-resolution cameras are employed in face recognition systems, the face recognition performance cannot be guaranteed for long range low-resolution images [1]. Processing speed for low-resolution face images, reliance on learning data, and reduced recognition rates according to changes in distance that occur during actual face recognition situations. There is a need for face recognition technology based on multiresolution that is robust to changes in resolution resulting from camera performance or changes in distance [2]. This paper will propose a face recognition system that employs face recognition learning according to virtual resolutions in order to resolve the issue of reduced recognition rates resulting from changes in face image resolution that occur through changes in distance.
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