Hybrid Algorithm for Face Recognition Using an Evolutionary Software Engineering

Abstract:

The principal aim was the construction of a face recognition system in order to be implemented in the service robot Donaxi, delimited by the Who is who test which is part of the RoboCups tests set, using an evolutionary development strategy of triple iterations. A two phase hybrid algorithm was developed, the first phase aim was the face detection using the Haar classifiers for face search in an image and the second phase is based on a decision tree whereby the faces characteristics were evaluated by the comparison techniques of phase correlation and histogram comparison. The needed characteristics were identified in order to develop this work as a software engineering project which allowed the algorithm construction and implementation through an evolutionary approach and a personal development process. The evolutionary strategy allowed the prototyping development with functionality and the tracking of the final system construction. A three iterations total was realized during which the needed metrics were registered (time, defects and sizes). The final analysis of results (algorithm and methods) allowed concluding and visualizing the employment advantages of a software engineering formal technique for research and robotics projects realization when improving estimations and software production quality.

• Periodical: Applied Mechanics and Materials (Volumes 423-426)
• Pages: 2543-2546
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.423-426.2543
• Online since: September 2013
• Authors: Hector Vargas*, Aldo Martinez
• Keywords: Evolutionary Software Engineering, Face Recognition, Haar Classifiers, Phase Correlatrions

References

[1] Merlin Dorfman, Richard H. Thayer, Software Engineering, Chapter 1: Issues — The Software Crisis, November 1996, Wiley-IEEE Computer Society Press.

[2] Martínez Aldo, (2010). Sistema de visión y voz para un robot de servicio: Nanisha, Universidad Popular Autónoma del Estado de Puebla.

DOI: 10.15741/revbio.04.05.05

[3] Humphrey Watts S, Introduction to the Personal Software Process, Carnegie Mellon University, 1997, Addison Wesley, ISBN: 0-201-548097.

[4] Humphrey Watts S, Introduction to the Team Software Process, Software Engineering Institute, 2000, Addison Wesley, ISBN: 0-201-47719X.

[5] BrugaliDavide, Software Engineering for Experimental Robotics, 2007, Springer Berlin / Heidelberg, ISSN 1610-7438 (Print) 1610-742X (Online), ISBN 978-3-540-68949-2.

[6] Castillo Jaime, Ciclos de Vida de Software, CITIS.

[7] Sommerville Ian, Software Engineering, 6th Edition, Addison Wesley, 2006, ISBN-10: 0321313798 | ISBN-13: 978-0321313799.

[8] Viola, P. y Jones, M. Rapid object detection using boosted cascade of simple features, IEEE Conference on Computer Vision and Pattern Recognition, (2001).

DOI: 10.1109/cvpr.2001.990517

[9] Rainer Lienhart and Jochen Maydt, An Extended Set of Haar-like Features for Rapid Object Detection, Intel Labs, Intel Corporation, Santa Clara, CA 95052, USA.

DOI: 10.1109/icip.2002.1038171

[10] B. S Reddy and B. N. Chatterji, An FFT-based technique for translation, rotation, and scale-invariant image registration, IEEE Transactions on Image Processing 5, no. 8 (1996): 1266–1271.

DOI: 10.1109/83.506761

[11] Villa Palacios, Sandra María, Sistema de Reconocimiento de Rostros, Universidad Peruana de Ciencias Aplicadas (UPC).

DOI: 10.19083/978-612-318-425-4

[12] Moreno Juan, Gómez J. Francisco, Fernández A. Miguel, Fernández Caballero, Reconocimiento de Rostros Utilizando Secuencias de Histogramas como Tramas Espacio-Temporales, Universidad de Castilla, la Mancha, Albacete España. SIARP (1999).

[13] Vadakkepat Prahlad, Lim Peter, De Silva Liyanage, Jing Liu, Ling, Li Li, Multimodal Approach to Human-Face Detection and Tracking, IEEE Transactions on Industrial Electronics, Vol. 55, No. 3, March (2008).

DOI: 10.1109/tie.2007.903993