System level design of real time face recognition architecture based on composite PCA

ABSTRACT

Design and implementation of a fast parallel architecture based on an improved principal component analysis (PCA) method called Composite PCA suitable for real-time face recognition is presented in this paper. The proposed architecture performs the tasks of both feature extraction and classification. Composite PCA takes in to consideration the local features of face images, which do not vary widely between face images of the same person taken under varying expression, illumination and pose. Hence it leads to a better recognition rate than PCA. Composite PCA has more parallelism than conventional PCA and this parallelism is utilized to design an efficient architecture capable of performing real-time face recognition. The face recognition system is implemented in an FPGA environment and tested using standard databases. The system is able to recognize a person from a database of 110 images of 10 individuals in approximately 4 ms.

REFERENCES

• 1: R. Chellappa, C. L. Wilson, and S. Sirohey. Human and machine recognition of faces: a survey. Proc. of IEEE, vol. 83, No. 5, pp 705--740, May 1995.
• 2: R. Brunelli , T. Poggio, Face Recognition: Features Versus Templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.15 n.10, p.1042-1052, October 1993 [doi>10.1109/34.254061]
• 3: Laurenz Wiskott , Jean-Marc Fellous , Norbert Krüger , Christopher von der Malsburg, Face Recognition by Elastic Bunch Graph Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.7, p.775-779, July 1997 [doi>10.1109/34.598235]
• 4: M. Turk and A. Pentland. Face recognition using eigenfaces. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586--591, 1991.
• 5: Peter N. Belhumeur , João P. Hespanha , David J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.19 n.7, p.711-720, July 1997 [doi>10.1109/34.598228]
• 6: M. Kirby , L. Sirovich, Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.12 n.1, p.103-108, January 1990 [doi>10.1109/34.41390]
• 7: D. B. Graham and N. M. Allinson. Characterizing Virtual Eigensignatures for General Purpose Face Recognition. Face Recognition: From Theory to Applications, NATO ASI Series F, Computer and Systems Sciences, Vol. 163.
• 8: http://cvc.yale.edu/projects/yalefaces/yalefaces.html
• 9: http://images.ee.umist.ac.uk/danny/database.html

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.3 SPECIAL-PURPOSE AND APPLICATION-BASED SYSTEMS
      Subjects: Signal processing systems

General Terms:
Algorithms, Design, Security

Keywords:
face recognition, parallel computer architectures, principal component analysis