Real-Time Face Detection and Recognition in Complex Background

• 作者: Jafar Saniie Thomas Gonnot Xin Zhang
• 来源期刊: 信号与信息处理(英文)
• 学科: 工学
• 关键词: Face Detection FACIAL Recognition ADA BOOST Algorithm CASCADE CLASSIFIER Local BINARY Pattern Haar-Like Features Principal Component Analysis
• 年，卷（期）: 2017,（2）
• 页码范围: 99-112
• 页数: 14页
• 分类号: TP39

摘要

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.