Abstract
Clustering is an important technique in data mining. It separates data points into different groups or clusters in such a way that objects in the same group are more similar to each other in some sense than with the objects in other groups. Gabor face clustering using affinity propagation and structural similarity index is composed of: A representation based on Gabor filters which has been shown to perform very well in face features, Affinity propagation clustering algorithm which is flexible, high speed, and does not require to specify the number of clusters, and structural similarity index which is a very powerful method for measuring the similarity between two images. Experimental results on two benchmark face datasets (LFW and IJB-B) show that our method outperforms well known clustering algorithms such as k-means, spectral clustering and Agglomerative.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bhattarai B, Sharma G, Jurie F, P’erez P (2014) “Some faces are more equal than others: Hierarchical organization for accurate and efficient large-scale identity-based face retrieval,” in ECCV Workshops, pp. 160–172. 2
Cao Z, Yin Q, Tang X, Sun J (2010) “Face recognition with learning based descriptor,” in Proc. CVPR. IEEE, pp. 2707–2714. 2
Cui J, Wen F, Xiao R, Tian Y, Tang X (2007) “Easyalbum: an interactive photo annotation system based on face clustering and re-ranking,” in Proc. of the SIGCHI conference on Human factors in computing systems. ACM, pp. 367–376. 2
Dueck D (2009) ‘Affinity propagation: clustering data by passing messages’, PhD, University of Toronto
Geng Du, Gong Liming Fei Su (2009). “An effective Gabor-feature selection method for face recognition,” 2009 IEEE International Conference on Network Infrastructure and Digital Content, Beijing, pp. 722–725
Ho J, Yang MH, Lim J, Lee KC, Kriegman D (2003) “Clustering appearances of objects under varying illumination conditions,” in Proc. CVPR IEEE. 2, 3
Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In Workshop on Faces in Real-Life Images: Detection, Alignment, and Recognition. 5
Li H, He F, Liang Y, Quan Q (2020) A dividing-based many-objective evolutionary algorithm for large-scale feature selection. Soft Comput 24:6851–6870
Liu T, Rosenberg C, Rowley HA (2007) “Clustering billions of images with large scale nearest neighbor search,” in Proc. WACV. IEEE, pp. 28–28. 3
Mart’ınez A, Benavente R (1998) “The extended AR Face Database,“Technical Report 24, Computer Vision Center, Bellatera,Barcelona, Spain
Sarwas G, Skoneczny S (2018) “FSIFT based feature points for face hierarchical clustering,” 2018 signal processing: algorithms, architectures, arrangements, and applications (SPA), Poznan, pp. 55-58. L
Schroff F, Kalenichenko D, Philbin J (2015) “Facenet: A unified embedding for face recognition and clustering,” in Proc. CVPR. 3
Shi Y, Otto C, Jain AK (2018) Face clustering: representation and pairwise constraints. in IEEE Transactions on Information Forensics and Security 13(7):1626–1640
Tapaswi M, Parkhi OM, Rahtu E, Sommerlade E, Stiefelhagen R, Zisserman A (2014) “Total cluster: A person agnostic clustering method for broadcast videos,” in Proc. of the Indian Conference on Computer Vision Graphics and Image Processing. ACM, p. 7. 2
Tian Y, Liu W, Xiao R, Wen F, Tang X (2007) “A face annotation framework with partial clustering and interactive labeling,” in Proc. CVPR IEEE. 2
Vidal R, Favaro P (2014) Low rank subspace clustering (lrsc). Pattern Recogn Lett 43(2):47–61
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. in IEEE Transactions on Image Processing 13(4):600–612
Wang J, Wang J, Zeng G, Tu Z, Gan R, Li S (2012) “Scalable k-NN graph construction for visual descriptors,” in Proc. CVPR. IEEE, pp. 1106–1113. 2
Wei P, Zhou Z, Li L, Jiang J (2018) Research on face feature extraction based on K-mean algorithm. J Image Video Proc 2018:83
Whitelam C, Taborsky E, Blanton A, Maze B, Adams J,Miller T, Kalka N, Jain AK, Duncan JA, Allen K, Cheney J, Grother P (2017) Iarpa janus benchmark-b face dataset.In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, July. 6
Yu H, He F, Pan Y (2020) A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation. Multimed Tools Appl 79:5743–5765
Zerabi S, Meshoul S (2017) “External clustering validation in big data context,” 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat, pp. 1–6
Zha M, Teo Y, Liu S, Chua T, Jain R (2006) “Automatic person annotation of family photo album,” in Image and Video Retrieval. Springer, pp. 163–172. 2, 3
Zhang S, He F (2019) DRCDN: learning deep residual convolutional dehazing networks. Vis Comput
Zhang J, He F, Chen Y (2020) A new haze removal approach for sky/river alike scenes based on external and internal clues. Multimed Tools Appl 79:2085–2107
Zhang C Li, H Wang, Shi M (2018), “Research On Face Image Clustering Based On Integrating Som And Spectral Clustering Algorithm,” 2018 International conference on machine learning and cybernetics (ICMLC), Chengdu, pp. 337–342
Zhu C, Wen F, Sun J (2011). “A rank-order distance based clustering algorithm for face tagging,” in Proc. CVPR. IEEE, pp. 481–488. 1, 2, 3, 4
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dagher, I., Mikhael, S. & Al-Khalil, O. Gabor face clustering using affinity propagation and structural similarity index. Multimed Tools Appl 80, 4719–4727 (2021). https://doi.org/10.1007/s11042-020-09822-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09822-5