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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.
Face tracking
Face detection
Rough fuzzy set
Neural clustering
Ellipse detection
This work was supported by MIUR—FIRB Project IntelliLogic (cod. RBIPO6MMBW) funded by
Minister of Education, University and Research of Italy.
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