Scalable Face Image Retrieval using Attribute based Search

Authors

  • Pittala Santosh Kumar Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, India
  • Mohammad Jaffer Sadiq Department of Information Technology, Sreenidhi Institute of Science and Technology, Hyderabad, India

Keywords:

Face Image proecssing, Face detection using Haarcascasdes, OpenCV, Facial fetrures extraction using FACESDK

Abstract

Photos are major interests of humans (e.g., family, friends, relatives, etc). Among all those photos, a big percentage of them are photos with human. With the exponentially growing images; Content-based Image Retrieval is an emerging application to retrieve the image from a large set of database. The goal of this research is to retrieve a face image based on attribute-based search. In this work, we aim to detect face image from a given input image and detected facial attributes that contain semantic cues of the face photos to improve content based face retrieval for efficient large-scale face retrieval. In my research, we are using OpenCV to detect the faces.

References

Y.-H. Lei, Y.-Y Chen, L. Iida, B.-C. Chen, H.-H. Su, and W.H. Hsu, “Photo search by face positions and facial attributes on touch devices,” ACM Multimedia, 2011.

D. Wang, S.C. Hoi, Y. He, and J.Zhu, “Retrieval-based face annotation by weak label regularized local coordinate coding,” ACM Multimedia, 2011.

Bor-Chun Chen, Yan-YIUNg Chen, Yin-His Kuo, and Winston H. Hsu, “Scalable face image retrieval using attribute enhanced sparse codewords,” IEEE Transaction on Multimedia, pp. 1163-1173, August 2013.

J. Zobel and A. Moffat, “Inverted files for text search engines,” ACM Compu. Surveys, 2006.

A. Gionis, P. Indyk, and R. Motwani, “Similiarity search in high dimensions via hasining,” VLDB, 1999.

J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” Int. Conf. Computer Vision, 2003.

D. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal Computer Vision, 2003.

L. Wu, S. C. H. Hoi, and N. Yu, “Semantics-preserving bag-of-words models and applications,” IEEE Trans. Image Process., vol. 19, no. 7, pp. 1908–1920, July 2010.

Y.-H. Kuo, H.-T. Lin, W.-H. Cheng, Y.-H. Yang, and W. H. Hsu, “Unsupervised auxiliary visual words discovery for large-scale image object retrieval,” IEEE Conf. Computer Vision and Pattern Recognition, 2011.

N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, “Describable visual attributes for face verification and image search,” IEEE Trans. Real-World Face Recognition, vol. 33, no. 10, pp. 1962–1977, Oct. 2011.

B. Siddiquie, R. S. Feris, and L. S. Davis, “Image ranking and retrieval based on multi-attribute queries,” IEEE Conf. Computer Vision and Pattern Recognition, 2011.

W. Scheirer, N. Kumar, K. Ricanek, T. E. Boult, and P. N. Belhumeur, “Fusing with context: A Bayesian approach to combining descriptive attributes,” in Proc. Int. Joint Conf. Biometrics, 2011.

Z. Wu, Q. Ke, J. Sun, and H.-Y. Shum, “Scalable face image retrieval with identity-based quantization and multi-reference re-ranking,” IEEE Conf. Computer Vision and Pattern Recognition, 2010.

B.-C. Chen, Y.-H. Kuo, Y.-Y. Chen, K.-Y. Chu, and W. Hsu, “Semi-supervised face image retrieval using sparse coding with identity constraint,” ACM Multimedia, 2011.

Adolf, F. “”ow-to build a cascade of boosted classifiers based on Haar-like features,” June 20 2003.

Paul Viola, Michael Jones, “Rapid object detection using a boosted cascade of simple features,” Conf. Computer Vision and Pattern Recognition, 2001.

Jackie Abbazio, Sasha Perez, Denise Silva, “Face Bio metric Ssytems,” IEEE Conf. Biometrics 2009.

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Published

2014-07-30

How to Cite

[1]
P. S. Kumar and M. J. Sadiq, “Scalable Face Image Retrieval using Attribute based Search”, Int. J. Comp. Sci. Eng., vol. 2, no. 7, pp. 20–23, Jul. 2014.

Issue

Section

Research Article