An Efficient Density-based clustering algorithm for face groping

Abstract This paper focuses on the following problem: Given a large number of unlabeled face images, group them into individual clusters, and the number of clusters cannot be known in advance. To this end, an Efficient Density-based clustering incorporated with the model of Graph partitioning (EDG) is proposed. 1. Inspired by the progress of graph partitioning clustering, a novel criterion that can be seen as a variant of the Normalized-cut model is employed to measure the similarity between two samples. 2. We only consider the similarities and connections on a subset of all possible pairs, i.e. the top-K nearest neighbors for each sample. Therefore, the computing and storage costs are linear w.r.t. the number of samples. In order to assess the performance of EDG on face images, extensive experiments based on a two-stage framework have been conducted on 19 benchmark datasets (14 middle-scale and 5 large-scale) from the literature. The experimental results have shown the effectiveness and robustness of our model, compared with the state-of-the-art methods.  [code]

[1]  Andreas Loukas,et al.  Approximating Spectral Clustering via Sampling: a Review , 2019, Sampling Techniques for Supervised or Unsupervised Tasks.

[2]  R. Janani,et al.  Text document clustering using Spectral Clustering algorithm with Particle Swarm Optimization , 2019, Expert Syst. Appl..

[3]  Chu-Song Chen,et al.  Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset , 2015, IEEE Transactions on Multimedia.

[4]  Qi Wang,et al.  Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification , 2019, Remote. Sens..

[5]  Bhanukiran Vinzamuri,et al.  A Survey of Partitional and Hierarchical Clustering Algorithms , 2018, Data Clustering: Algorithms and Applications.

[6]  Deng Cai,et al.  EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph , 2016, ArXiv.

[7]  Carlos D. Castillo,et al.  Deep Density Clustering of Unconstrained Faces , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Ruixuan Li,et al.  EADP: An extended adaptive density peaks clustering for overlapping community detection in social networks , 2019, Neurocomputing.

[9]  Weihong Deng,et al.  Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments , 2017, ArXiv.

[10]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[11]  M. Saquib Sarfraz,et al.  Efficient Parameter-Free Clustering Using First Neighbor Relations , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Rashi Jain,et al.  Image Segmentation Through Fuzzy Clustering: A Survey , 2018, Harmony Search and Nature Inspired Optimization Algorithms.

[13]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[15]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[16]  Luc Van Gool,et al.  Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks , 2016, International Journal of Computer Vision.

[17]  Jitendra Malik,et al.  Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Peter J. Mucha,et al.  Social clustering in epidemic spread on coevolving networks , 2017, Physical review. E.

[19]  Ankush Sharma,et al.  KNN-DBSCAN: Using k-nearest neighbor information for parameter-free density based clustering , 2017, 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT).

[20]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Shan-shan Li,et al.  An Improved DBSCAN Algorithm Based on the Neighbor Similarity and Fast Nearest Neighbor Query , 2020, IEEE Access.

[22]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[23]  Rong Wang,et al.  Scalable Graph-Based Clustering With Nonnegative Relaxation for Large Hyperspectral Image , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Ayyaz Hussain,et al.  Reliable facial expression recognition for multi-scale images using weber local binary image based cosine transform features , 2017, Multimedia Tools and Applications.

[25]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[26]  Vladlen Koltun,et al.  Robust continuous clustering , 2017, Proceedings of the National Academy of Sciences.

[27]  Gang Hua,et al.  Labeled Faces in the Wild: A Survey , 2016 .

[28]  Sajid Ali Khan,et al.  Face recognition under varying expressions and illumination using particle swarm optimization , 2018, J. Comput. Sci..

[29]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[30]  Ricardo J. G. B. Campello,et al.  Density-Based Clustering Based on Hierarchical Density Estimates , 2013, PAKDD.

[31]  Xin Liu,et al.  Fast density peak clustering for large scale data based on kNN , 2020, Knowl. Based Syst..

[32]  Michael Hahsler,et al.  dbscan: Fast Density-Based Clustering with R , 2019, Journal of Statistical Software.

[33]  Guoyin Wang,et al.  DenPEHC: Density peak based efficient hierarchical clustering , 2016, Inf. Sci..

[34]  Xuelong Li,et al.  Unsupervised Large Graph Embedding , 2017, AAAI.

[35]  Qi Song,et al.  A New DBSCAN Parameters Determination Method Based on Improved MVO , 2019, IEEE Access.

[36]  Yike Guo,et al.  Fast density clustering strategies based on the k-means algorithm , 2017, Pattern Recognit..

[37]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[38]  Xiaofei Zhang,et al.  Density Peak-Based Noisy Label Detection for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Tieniu Tan,et al.  A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.

[40]  Hong Wang,et al.  Shared-nearest-neighbor-based clustering by fast search and find of density peaks , 2018, Inf. Sci..

[41]  William Zhu,et al.  A New Local Density for Density Peak Clustering , 2018, PAKDD.

[42]  Tal Hassner,et al.  Effective face frontalization in unconstrained images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Wei-keng Liao,et al.  A Fast DBSCAN Algorithm with Spark Implementation , 2018 .

[44]  Anil K. Jain,et al.  Clustering Millions of Faces by Identity , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  Pravin Chandra,et al.  A Comparative Study of Clustering Algorithms , 2019, 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom).

[46]  Zhengming Ma,et al.  Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy , 2017, Knowl. Based Syst..

[47]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[48]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Avory Bryant,et al.  RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates , 2018, IEEE Transactions on Knowledge and Data Engineering.

[50]  Tal Hassner,et al.  Face recognition in unconstrained videos with matched background similarity , 2011, CVPR 2011.

[51]  Rama Chellappa,et al.  A Proximity-Aware Hierarchical Clustering of Faces , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).