Image Processing Reference
A rough fuzzy neural network
approach for robust face
detection and tracking
Alfredo Petrosino; Giuseppe Salvi University “Parthenope”, Naples, Italy
Automatic detection and tracking of human faces in video sequences are considered fundamental in
many applications, such as face recognition, video surveillance, and human-computer interface. In this
study, we propose a technique for real-time robust facial tracking in human facial videos based on a
new algorithm for face detection in color images. The proposed face-detection algorithm extracts skin
color regions in the CEI Lab color space, through the use of a specialized unsupervised neural network.
A correlation-based method is then applied for the detection of human faces as elliptic regions. As a
part of face tracking, the Kalman filter algorithm is used to predict the next face-detection window and
smooth the tracking trajectory. Experiments on the five benchmark databases, namely, the CMU-PIE,
color FERET, IMM, and CalTech face databases, and the standard IIT-NRC facial video database demon-
strate the ability of the proposed algorithm in detecting and tracking faces in difficult conditions as com-
plex background and uncontrolled illumination.
Rough fuzzy set
This work was supported by MIUR—FIRB Project IntelliLogic (cod. RBIPO6MMBW) funded by
Minister of Education, University and Research of Italy.