publication . Other literature type . Conference object . Preprint . Article . 2020

Person Recognition in Personal Photo Collections

Seong Joon Oh; Rodrigo Benenson; Mario Fritz; Bernt Schiele;
  • Published: 01 Jan 2020
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation. For human identification task, however, traditional focus of computer vision has been face recognition and pedestrian re-identification. Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e.g. backward viewpoints, unusual poses) and great changes in appearance. To tackle this problem, we build a simple person recognition framework that leverages convnet features from multiple image ...
Subjects
free text keywords: Artificial intelligence, business.industry, business, Person recognition, Clothing, Machine vision, Computer science, Human–computer interaction, Open data, Computer vision, Training set, Social media, Computational Theory and Mathematics, Software, Applied Mathematics, Computer Vision and Pattern Recognition, Viewpoints, Social group, Facial recognition system, Pedestrian, Multimedia, computer.software_genre, computer, Task analysis, Computer Science - Computer Vision and Pattern Recognition
Related Organizations
60 references, page 1 of 4

[1] N. Zhang, M. Paluri, Y. Taigman, R. Fergus, and L. Bourdev, “Beyond frontal faces: Improving person recognition using multiple cues,” in CVPR, 2015. [OpenAIRE]

[2] S. J. Oh, R. Benenson, M. Fritz, and B. Schiele, “Person recognition in personal photo collections,” in ICCV, 2015.

[3] Y. Sun, X. Wang, and X. Tang, “Deeply learned face representations are sparse, selective, and robust,” arXiv, 2014.

[4] G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” UMass, Tech. Rep., 2007.

[5] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in CVPR, 2014. [OpenAIRE]

[6] E. Zhou, Z. Cao, and Q. Yin, “Naive-deep face recognition: Touching the limit of lfw benchmark or not?” arXiv, 2015.

[7] F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” arXiv, 2015.

[8] O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” in BMVC, 2015.

[9] J.-C. Chen, V. M. Patel, and R. Chellappa, “Unconstrained face verification using deep cnn features,” in WACV. IEEE, 2016, pp. 1-9.

[10] Y. Wen, K. Zhang, Z. Li, and Y. Qiao, “A discriminative feature learning approach for deep face recognition,” in European Conference on Computer Vision. Springer, 2016, pp. 499-515.

[11] M. Guillaumin, J. Verbeek, and C. Schmid, “Is that you? metric learning approaches for face identification,” in ICCV, 2009. [OpenAIRE]

[12] D. Chen, X. Cao, F. Wen, and J. Sun, “Blessing of dimensionality: Highdimensional feature and its efficient compression for face verification,” in CVPR, 2013.

[13] X. Cao, D. Wipf, F. Wen, and G. Duan, “A practical transfer learning algorithm for face verification,” in ICCV, 2013. [OpenAIRE]

[14] C. Lu and X. Tang, “Surpassing human-level face verification performance on lfw with gaussianface,” arXiv, 2014.

[15] B. F. Klare, E. Taborsky, A. Blanton, J. Cheney, K. Allen, P. Grother, A. Mah, M. Burge, and A. K. Jain, “Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a,” algorithms, vol. 13, p. 4, 2015. [OpenAIRE]

60 references, page 1 of 4
Abstract
People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation. For human identification task, however, traditional focus of computer vision has been face recognition and pedestrian re-identification. Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e.g. backward viewpoints, unusual poses) and great changes in appearance. To tackle this problem, we build a simple person recognition framework that leverages convnet features from multiple image ...
Subjects
free text keywords: Artificial intelligence, business.industry, business, Person recognition, Clothing, Machine vision, Computer science, Human–computer interaction, Open data, Computer vision, Training set, Social media, Computational Theory and Mathematics, Software, Applied Mathematics, Computer Vision and Pattern Recognition, Viewpoints, Social group, Facial recognition system, Pedestrian, Multimedia, computer.software_genre, computer, Task analysis, Computer Science - Computer Vision and Pattern Recognition
Related Organizations
60 references, page 1 of 4

[1] N. Zhang, M. Paluri, Y. Taigman, R. Fergus, and L. Bourdev, “Beyond frontal faces: Improving person recognition using multiple cues,” in CVPR, 2015. [OpenAIRE]

[2] S. J. Oh, R. Benenson, M. Fritz, and B. Schiele, “Person recognition in personal photo collections,” in ICCV, 2015.

[3] Y. Sun, X. Wang, and X. Tang, “Deeply learned face representations are sparse, selective, and robust,” arXiv, 2014.

[4] G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” UMass, Tech. Rep., 2007.

[5] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in CVPR, 2014. [OpenAIRE]

[6] E. Zhou, Z. Cao, and Q. Yin, “Naive-deep face recognition: Touching the limit of lfw benchmark or not?” arXiv, 2015.

[7] F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” arXiv, 2015.

[8] O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” in BMVC, 2015.

[9] J.-C. Chen, V. M. Patel, and R. Chellappa, “Unconstrained face verification using deep cnn features,” in WACV. IEEE, 2016, pp. 1-9.

[10] Y. Wen, K. Zhang, Z. Li, and Y. Qiao, “A discriminative feature learning approach for deep face recognition,” in European Conference on Computer Vision. Springer, 2016, pp. 499-515.

[11] M. Guillaumin, J. Verbeek, and C. Schmid, “Is that you? metric learning approaches for face identification,” in ICCV, 2009. [OpenAIRE]

[12] D. Chen, X. Cao, F. Wen, and J. Sun, “Blessing of dimensionality: Highdimensional feature and its efficient compression for face verification,” in CVPR, 2013.

[13] X. Cao, D. Wipf, F. Wen, and G. Duan, “A practical transfer learning algorithm for face verification,” in ICCV, 2013. [OpenAIRE]

[14] C. Lu and X. Tang, “Surpassing human-level face verification performance on lfw with gaussianface,” arXiv, 2014.

[15] B. F. Klare, E. Taborsky, A. Blanton, J. Cheney, K. Allen, P. Grother, A. Mah, M. Burge, and A. K. Jain, “Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a,” algorithms, vol. 13, p. 4, 2015. [OpenAIRE]

60 references, page 1 of 4
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publication . Other literature type . Conference object . Preprint . Article . 2020

Person Recognition in Personal Photo Collections

Seong Joon Oh; Rodrigo Benenson; Mario Fritz; Bernt Schiele;