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.

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

[1] 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.
[2] D. Wang, S.C. Hoi, Y. He, and J.Zhu, �Retrieval-based face annotation by weak label regularized local coordinate coding,� ACM Multimedia, 2011.
[3] 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.
[4] J. Zobel and A. Moffat, �Inverted files for text search engines,� ACM Compu. Surveys, 2006.
[5] A. Gionis, P. Indyk, and R. Motwani, �Similiarity search in high dimensions via hasining,� VLDB, 1999.
[6] J. Sivic and A. Zisserman, �Video Google: A text retrieval approach to object matching in videos,� Int. Conf. Computer Vision, 2003.
[7] D. Lowe, �Distinctive image features from scale-invariant keypoints,� International Journal Computer Vision, 2003.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] Adolf, F. ��ow-to build a cascade of boosted classifiers based on Haar-like features,� June 20 2003.
[16] Paul Viola, Michael Jones, �Rapid object detection using a boosted cascade of simple features,� Conf. Computer Vision and Pattern Recognition, 2001.
[17] Jackie Abbazio, Sasha Perez, Denise Silva, �Face Bio metric Ssytems,� IEEE Conf. Biometrics 2009.