Fast and robust occluded face detection in ATM surveillance

Abstract Crimes with respect to ATMs (Automatic Teller Machines) have attracted more and more attention, where criminals deliberately cover their faces in order to avoid being identified. This paper proposes a fast and robust face occlusion detection algorithm for ATM surveillance, which is demonstrated to be effective and efficient to handle arbitrarily occluded faces. In this algorithm, we innovatively propose to make use of the Omega shape formed by the head and shoulder of the person for head localization to tackle severe face occlusion. For this purpose, we first construct a novel energy function for elliptical head contour detection. Then, we develop a fast and robust head tracking algorithm, which utilizes the gradient and shape cues in a Bayesian framework. Lastly, to verify whether a detected head is occluded or not, we propose to fuse information from both skin color and face structure using the AdaBoost algorithm. Experimental results on real world data show that our proposed algorithm can achieve 98.64% accuracy on face detection and 98.56% accuracy on face occlusion detection, even though there are severe occlusions in faces, at a speed of up to 12 frames per second.

[1]  Hiroshi Katsulai,et al.  Evaluation of Image Fidelity by Means of the Fidelogram and Level Mean-Square Error , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Wentao Dong,et al.  Image-based Fraud Detection in Automatic Teller Machine , 2006 .

[3]  K. Balanda,et al.  Kurtosis: A Critical Review , 1988 .

[4]  Premanand K. Kadbe,et al.  Real time finger tracking and contour detection for gesture recognition using OpenCV , 2015, 2015 International Conference on Industrial Instrumentation and Control (ICIC).

[5]  Jing Liu,et al.  Robust Structured Subspace Learning for Data Representation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Zhengrong Yao,et al.  Tracking a Detected Face with Dynamic Programming , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[7]  Huitao Luo,et al.  Model-based segmentation and tracking of head-and-shoulder video objects for real time multimedia services , 2003, IEEE Trans. Multim..

[8]  Souvik Das,et al.  An intelligent vision system for monitoring security and surveillance of ATM , 2015, 2015 Annual IEEE India Conference (INDICON).

[9]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Ho Gi Jung,et al.  Recognizability assessment of facial images for automated teller machine applications , 2012, Pattern Recognit..

[11]  Dacheng Tao,et al.  A Comprehensive Survey on Pose-Invariant Face Recognition , 2015, ACM Trans. Intell. Syst. Technol..

[12]  Rama Chellappa,et al.  Pose-Invariant Face Recognition Using Markov Random Fields , 2013, IEEE Transactions on Image Processing.

[13]  Hans A. Kestler,et al.  Generalized Venn diagrams: a new method of visualizing complex genetic set relations , 2005, Bioinform..

[14]  Abdenour Hadid,et al.  Efficient Detection of Occlusion prior to Robust Face Recognition , 2014, TheScientificWorldJournal.

[15]  Lei Huang,et al.  Robust skin detection in real-world images , 2015, J. Vis. Commun. Image Represent..

[16]  Nikolaos G. Bourbakis,et al.  A survey of skin-color modeling and detection methods , 2007, Pattern Recognit..

[17]  Kevin Curran,et al.  A skin tone detection algorithm for an adaptive approach to steganography , 2009, Signal Process..

[18]  Julio Jacobo-Berlles,et al.  Face recognition on partially occluded images using compressed sensing , 2014, Pattern Recognit. Lett..

[19]  Sargur N. Srihari,et al.  Progressive Refinement of 3-D Images Using Coded Binary Trees: Algorithms and Architecture , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Sam Kwong,et al.  Efficient Motion and Disparity Estimation Optimization for Low Complexity Multiview Video Coding , 2015, IEEE Transactions on Broadcasting.

[21]  Jie Lin,et al.  Robust face recognition with partial occlusion, illumination variation and limited training data by optimal feature selection , 2011 .

[22]  Xu-Dong Zhang,et al.  Learning to Rank from Noisy Data , 2015, ACM Trans. Intell. Syst. Technol..

[23]  Che-Yen Wen,et al.  The mask detection technology for occluded face analysis in the surveillance system. , 2005, Journal of forensic sciences.

[24]  Roland Göcke,et al.  Finding Happiest Moments in a Social Context , 2012, ACCV.

[25]  Sarajane Marques Peres,et al.  Face recognition using Support Vector Machine and multiscale directional image representation methods: A comparative study , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[26]  Ruijiang Luo,et al.  Real time head tracking and face and eyes detection , 2002, 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings..

[27]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  K. G. Subramanian,et al.  Recognizing occluded faces by exploiting psychophysically inspired similarity maps , 2013, Pattern Recognit. Lett..

[29]  Cheng Lu,et al.  On the removal of shadows from images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Abhijith Punnappurath,et al.  Recognizing blurred, nonfrontal, illumination, and expression variant partially occluded faces. , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[31]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[32]  Ruey-Song Huang,et al.  Facial model estimation from stereo/mono image sequence , 2003, IEEE Trans. Multim..

[33]  Dacheng Tao,et al.  Pose-invariant face recognition with homography-based normalization , 2017, Pattern Recognit..

[34]  Peng Jin,et al.  Fast reference frame selection based on content similarity for low complexity HEVC encoder , 2016, J. Vis. Commun. Image Represent..

[35]  Jongsun Kim,et al.  Effective representation using ICA for face recognition robust to local distortion and partial occlusion , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Thomas Serre,et al.  A Component-based Framework for Face Detection and Identification , 2007, International Journal of Computer Vision.

[37]  Xingming Sun,et al.  Fast Motion Estimation Based on Content Property for Low-Complexity H.265/HEVC Encoder , 2016, IEEE Transactions on Broadcasting.

[38]  Sanja Fidler,et al.  Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Dacheng Tao,et al.  Robust Face Recognition via Multimodal Deep Face Representation , 2015, IEEE Transactions on Multimedia.

[40]  Chengjun Liu,et al.  A Bayesian Discriminating Features Method for Face Detection , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Andrew Zisserman,et al.  Progressive search space reduction for human pose estimation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Inho Choi,et al.  Facial Fraud Discrimination Using Detection and Classification , 2010, ISVC.

[44]  Slobodan Ribaric,et al.  Deformable part-based robust face detection under occlusion by using face decomposition into face components , 2016, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).