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.
Highlights
Real-time face detection and facial recognition play an important role in applications such as robot intelligence, smart cameras, security monitoring or even criminal identification
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)
Summary
Real-time face detection and facial recognition play an important role in applications such as robot intelligence, smart cameras, security monitoring or even criminal identification. Conventional algorithms for face detection and facial recognition are designed for still-face images or color images. The colors increase data complexity by mapping pixels onto a high-dimensional space, which greatly reduces the processing speed and accuracy of the face detection and recognition [1]
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