Maturana, D., Mery, D. and Soto, A. (2009) Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes nearest Neighbor Classification. 2009 International Conference of the Chilean Computer Science Society, Santiago, 10-12 November 2009, 125-132.
https://doi.org/10.1109/SCCC.2009.21

has been cited by the following article:

• TITLE: Real-Time Face Detection and Recognition in Complex Background

  AUTHORS: Xin Zhang, Thomas Gonnot, Jafar Saniie

  KEYWORDS: Face Detection, Facial Recognition, Ada Boost Algorithm, Cascade Classifier, Local Binary Pattern, Haar-Like Features, Principal Component Analysis

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.8 No.2, May 19, 2017

  ABSTRACT: This paper provides efficient and robust algorithms for real-time face detection and recognition in complex backgrounds. The algorithms 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 and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a cascade classifier to train the face and eye detectors with robust detection accuracy. The LBP descriptor is utilized to extract facial features for fast face detection. The eye detection algorithm reduces the false face detection rate. The detected facial image is then processed to correct the orientation and increase the contrast, therefore, maintains high facial recognition accuracy. Finally, the PCA algorithm is used to recognize faces efficiently. Large databases with faces and non-faces images are used to train and validate face detection and facial recognition algorithms. The algorithms achieve an overall true-positive rate of 98.8% for face detection and 99.2% for correct facial recognition.