| dc.description.abstract | Face detection plays an important role in indoor surveillance systems. Although there have
been many improvements since the 90s, many problems are still unsolved due to the complexity
of background, complex computation, long time execution, illumination changes, etc. This paper
presents a method to detect faces in a surveillance system under real-world indoor condition. The
proposed method combines Background Subtraction Model Feature Analysis approach and Image
base approach to localize faces. Many experiments have been conducted with several indoor
datasets to evaluate the performance and accuracy of the proposed methods. The result achieves a
detection rate of 97.072% and processing speed more than 30 frames per seconds in average.
Keywords: face detection, indoor surveillance system, background subtraction, local
binary pattern cascade classifier, single shot multi box detection (SSD) face detector, face
recognition. | en_US |
