Face Recognition Technology And Its Embedded Applications

In the past several decades, significant progress has been made in the field of computer vision and machine learning. The learning algorithm has been changed from rule-based to statistics-based. What’s more, as the rapid increase in computer hard-ware performance and the rapid decrease in the computer hardware cost, face recogni-tion are being used in more and more applications. For example, access control sys-tems, the application of a variety of mobile terminals, smart monitoring and so on. Based on the face recognition technology, we did intensive research in the field of al-gorithm design, algorithm optimization, optimization for specific platforms.Firstly, the key steps of the Viola-Jones face detector, including the Haar-feature-based weak classifier, the AdaBoost-based strong classifier and the at-tentional cascade are studied. And we optimized the algorithm in detail. The experi-mental results show the great improvement in the efficiency, and a slight increasement in the detection rate.Secondly, a framework for eye detection is presented,which divide eye detection into two steps, including coarse detection and fine detection. The coarse detection can be implemented by Viola-Jones detector. The fine detection can be implemented by either Viola-Jones-based verification or mean shift method. These two different me-thods are suitable for different scenarios respectively.Thirdly, after deep-study of the affine transformation, a novel method for face normalization based on correlation between adjacent pixels is proposed. The experi-mental results show significant improvement in the efficiency of the normalization.Fourthly, to speed up the proposed Gabor-Orientation-Histogram-based face ve-rification method, FFTW is used in the implementation. Experimental results show that the FFTW-based method increase the processing speed to18%of the original time.Fifthly, the implementation of the face recognition in the C++programming language and the C programming language is completed. And we optimize the calcu-lus from float-point to fixed-point. In addition, by the use of Android NDK, the face recognition system can be invoked by Android applications.