Face Detection And Modeling For Face Recognition
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Face Detection and Modeling for Recognition
Face recognition has received substantial attention from researchers in biometrics, computer vision, pattern recognition, and cognitive psychology communities because of the increased attention being devoted to security, man-machine communication, content-based image retrieval, and image/video coding. We have proposed two automated recognition paradigms to advance face recognition technology. Three major tasks involved in face recognition systems are: (i) face detection, (ii) face modeling, and (iii) face matching. We have developed a face detection algorithm for color images in the presence of various lighting conditions as well as complex backgrounds. Our detection method first corrects the color bias by a lighting compensation technique that automatically estimates the parameters of reference white for color correction. We overcame the difficulty of detecting the low-luma and high-luma skin tones by applying a nonlinear transformation to the Y CbCr color space. Our method generates face candidates based on the spatial arrangement of detected skin patches. We constructed eye, mouth, and face boundary maps to verify each face candidate. Experimental results demonstrate successful detection of faces with different sizes, color, position, scale, orientation, 3D pose, and expression in several photo collections. 3D human face models augment the appearance-based face recognition approaches to assist face recognition under the illumination and head pose variations. For the two proposed recognition paradigms, we have designed two methods for modeling human faces based on (i) a generic 3D face model and an individual's facial measurements of shape and texture captured in the frontal view, and (ii) alignment of a semantic face graph, derived from a generic 3D face model, onto a frontal face image.
Face Detection and Modeling for Face Recognition
The primary aim of this research work is to study and implement Viola and Jones [3] based face detector, and provide possible improvement to the original algorithm. It also studies its possible integration with real time face recognition system. Recently, many new face detection algorithms have been published based on original Viola and Jones' face detector. Viola and Jones provided three novel approaches for constructing a rapid and robust real time face detection system. Using Haar-like features with unique integral image representation the computation of the features becomes extremely rapid. Using AdaBoost a strong non-linear classifier can be constructed, and only useful features out of large feature set can be extracted and used for face detection. Cascade of classifiers provides very positive solution for improvement of execution speed and also improves the accuracy of the whole system. By cascading classifiers, each stage is trained separately using different feature, in other word each stage focuses on particular feature. Using this technique a very rapid face detector can be created and its results are comparable with other state-of-the art technique. To understand the role of face detection system in real time face recognition system eigenfaces based real-time face recognition is evaluated.
Handbook of Face Recognition
Author: Stan Z. Li
language: en
Publisher: Springer Science & Business Media
Release Date: 2005-12-06
Although the history of computer-aided face recognition stretches back to the 1960s, automatic face recognition remains an unsolved problem and still offers a great challenge to computer-vision and pattern recognition researchers. This handbook is a comprehensive account of face recognition research and technology, written by a group of leading international researchers. Twelve chapters cover all the sub-areas and major components for designing operational face recognition systems. Background, modern techniques, recent results, and challenges and future directions are considered. The book is aimed at practitioners and professionals planning to work in face recognition or wanting to become familiar with the state-of- the-art technology. A comprehensive handbook, by leading research authorities, on the concepts, methods, and algorithms for automated face detection and recognition. Essential reference resource for researchers and professionals in biometric security, computer vision, and video image analysis.