Face Detection and Recognition Based on Facial Features and Key Points Matching

Basava Raju*, K. Y. Rama Devi**, P. V. Kumar***
* Research Scholar, Jawaharlal Nehru Technological University, Kakinada, AP, India.
** HOD, Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad.
*** Professor, Department of Computer Science and Engineering, Osmania University, Hyderabad, Telangana.
Periodicity:March - May'2015
DOI : https://doi.org/10.26634/jpr.2.1.3370

Abstract

Face Recognition has benefitted greatly from the many databases that have been produced to study it. Most of these databases have been created under controlled conditions to facilitate the study of specific parameters on the Face Recognition problem. These parameters include such variables as Position, Pose, Lighting, Expression, Background, Camera Quality, Occlusion, Age, and Gender. While there are many applications for Face Recognition Technology in which one can control the parameters of Image Acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This paper is provided as an aid in studying the latter, unconstrained, face recognition problem. The database represents an initial attempt to provide a set of labeled face photographs spanning the range of conditions typically encountered by people in their everyday lives. This paper describes a face detection system which goes beyond traditional face detection approaches normally designed for single faced images. The system described in this paper has been designed taking into account spatial coherence contained in multiple face detection. The resulting system builds a feature based model for each detected face, and searches them using various model information in the database. It provides a feasible way to locate the positions of two eyeballs, near and far corners of eyes, midpoint of nostrils and mouth corners from face image. This approach would help to extract useful features on human face automatically and improve the accuracy of face recognition.

Keywords

Face Detection, Face Recognition, Feature Localization, Unconstrained Faces.

How to Cite this Article?

Raju, K. B., Ramadevi, Y., and Kumar, P. V. (2015). Face Detection and Recognition Based on Facial Features and Key Points Matching. i-manager’s Journal on Pattern Recognition, 2(1),1-9. https://doi.org/10.26634/jpr.2.1.3370

References

[1]. R. Brunelli and T. Poggio, (1993). “Face Recognition:Features Versus Templates”, IEEE Transaction on PAMI, Vol. 15, No. 10, pp. 1042-1052.
[2]. S. Y. Lee, Y. K. Ham and R. H. Park, (1996). “Recognition of Human Front Faces using Knowledge-Based Feature Extraction and NeuroFuzzy Algorithm”, Pattern Recognition, Vol. 29, No.11, pp. 1863-1876.
[3]. R. S. Feris, T. E. de Campos and R.M. Cesar Junior, (2000). “Detection and tracking of facial features in video sequences”, Lecture Notes in Artificial Intelligence, Vol.1793, No. 4, pp. 127-135.
[4]. G. Chow and X. Li, (1993). “Towards A System for Automatic Facial Feature Detection”, Pattern Recognition, Vol. 26, No. 12, pp. 1739-1755.
[5]. M. Gargesha and S. Panchanathan, (2002). “A Hybrid Technique for Facial Feature Point Detection”, IEEE Proceedings of SSIAI' 2002, pp. 134-138.
[6]. R. S. Feris, J. Gemmell, K. Toyama and V. Kruger, (2002). “Hierarchical Wavelet Networks for Facial Feature Localization”, Proceedings of the Fifth IEEE International Conference on AutomaticFace and Gesture Recognition (FGR.02) , pp. 118-123.
[7]. E. Hjelmas, B.K. (2001). “Low, Face Detection: A Survey”, Computer Vision and Image Understanding, Vol.83, No. 3, pp. 236–274.
[8]. M.-H. Yang, D. Kriegman, N. Ahuja, (2002). “Detecting Faces in Images: A Survey”, Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp. 34–58.
[9]. P. Viola, M.J. Jones (2002). “Robust Real-Time Face Detection”, International Journal of Computer Vision, Vol. 57, No. 2, pp. 151–173.
[10]. H. Schneiderman, T. Kanade, (2000). “A Statistical Method for 3D Object Detection Applied to Faces and Cars”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1746–1759.
[11]. P. Viola, M.J. Jones, (2004). “Robust Real-Time Face Detection”, International Journal of Computer Vision, Vol. 57, No. 2, pp. 151–173.
[12]. R. Lienhart, A. Kuranov, V. Pisarevsky, (2003). “Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection”, DAGM'03, Magdeburg, Germany, pp. 297–304.
[13]. Intel, Intel Open Source Computer Vision Library, b4.0, (August 2004)
[14]. C. Yan and G. D. Su, (1998). “Facial Feature Location and Extraction From Front-View Images”, Journal of Image and Graphics of China, Vol. 3, No. 5, pp. 375-380.
[15]. M. Brown and D.G. Lowe, (2002). “Invariant Features From Interest Pointgroups,” British Machine Vision Conference, pp. 656-665.
[16]. D.G. Lowe, (2004). “Distinctive Image Features From Scale-Invariant Keypoints,” International Journal of Computer Vision, Vol. 60, pp. 91-110.
[17]. T. Lindeber, (1998). “Feature Detection with Automatic Scale Selection,” International Journal of Computer Vision, Vol. 30, No.2, pp. 79-116.

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