Real-Time Face Detection and Recognition in Complex Background

Haar-like features Local Binary Patterns Three-dimensional face recognition Object-class detection Cascading classifiers Face hallucination Feature (linguistics)
DOI: 10.4236/jsip.2017.82007 Publication Date: 2017-05-19T09:36:14Z
ABSTRACT
This paper provides efficient and robust algorithms for real-time face detection recognition in complex backgrounds. The are implemented using a series of signal processing methods including Ada Boost, cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing Principal Component Analysis (PCA). Boost algorithm is classifier to train the eye detectors with accuracy. LBP descriptor utilized extract features fast detection. reduces false rate. detected then processed correct orientation increase contrast, therefore, maintains high Finally, PCA used recognize faces efficiently. Large databases non-faces images validate algorithms. achieve an overall true-positive rate 98.8% 99.2% recognition.
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