Abstract  Systems  that  rely  on  Face  Recognition  (FR)  biometri c  have  gained  great  importance  ever  since  terrorist  threats  imposed  weakness  among  the  implemented  security  sys tems.  Other  biometrics  i.e.,  fingerprints  or  iris  recognition  is  not  trustworthy  in  such  situations  whereas  FR  is  consid ered  as  a  fine  compromise.  This  survey  illustrates different  FR  practices  that  laid  foundations  on  the  issue  of  partial  occlu sion  dilemma  where  faces  are  disguised  to  cheat  the   security  system.  Occlusion  refers  to  facade  of  the  face  image  which  can  be  due  to  sunglasses,  hair  or  wrapping  of  facial  image  by  scarf  or  other  accessories.  Efforts  on  FR  in  controlled  sett ings  have  been  in  the  picture  for  past  several  year s;  however  identification  under  uncontrolled  conditions  like  illumination,  ex pression  and  partial  occlusion  is  quite  a  matter  of   concern.  Based  on  literature  a  classification  is  made  in  this  paper  t o  solve  the  recognition  of  face  in  the  presence  of  partial  occlusion.  These  methods  are  named  as  part  based  methods  that  make  u se  of  Principal  Component  Analysis  (PCA),  Linear  Discriminate  Analysis  (LDA),  Non-negative  Matrix  Factorization  ( NMF),  Local  Non-negative  Matrix  Factorization  (LNMF ),  Independent  Component Analysis (ICA) and other variations. Feat ure based and fractal based methods consider features around eyes, nose  or  mouth  region  to  be  used  in  the  recognition  phase   of  algorithms.  Furthermore  the  paper  details  the  experiments  and  databases  used  by  an  assortment  of  authors  to  handl e  the  problem  of  occlusion  and  the  results  obtained  after  performing  diverse  set  of  analysis.  Lastly,  a  comparison  of  va rious  techniques  is  shown  in  tabular  format  to  give   a  precise  overview  of  what different authors have already projected in th is particular field.     

References

[1] Abate A., Nappi M., Riccio D., and Tucci M., Occluded Face Recognition by Means of the IFS, in Proceedings of the 2 nd International Conference Image Analysis and Recognition , Berlin, pp. 1073-1080, 2005.

[2] Aleix M., Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample Per Class, IEEE Transaction on Pattern Analysis and Machine Intelligence , vol. 24, no. 6, pp. 748-763, 2002.

[3] Amirhosein N., Esam A., and Majid A., Illumination Invariant Feature Extraction and Mutual-Information-Based Local Matching for Face Recognition under Illumination Variation and Occlusion, Pattern Recognition , vol. 44, no. 10-11, pp. 2576-2587, 2011.

[4] Baudat G. and Anouar F., Generalized Discriminant Analysis using a Kernel Approach, Neural Computation , vol. 12, no. 10, pp. 2385- 2404, 2000.

[5] Benjamin C., David B., Philip M., and Jitendra M., A Real-Time Computer Vision System for Vehicle Tracking and Traffic Surveillance, Transportation Research Part C: Emerging Technologies , vol. 6, no. 4, pp. 271-288, 1998.

[6] Brunelli R. and Poggio T., Face Recognition: Features Versus Template, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 15, no. 10, pp. 1042-1052, 1993.

[7] Chellappa R., Wilson C., and Sirohey S., Human and Machine Recognition of Faces: A Survey, in Proceedings of IEEE , vol. 83, no. 5, pp. 705-740, 1995.

[8] Chen W., Yuen P., Huang J., and Dai D., Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , vol. 35, no. 4, pp. 659-669, 2005.

[9] Ciocoiu B., Occluded Face Recognition using Parts Based Representation Methods, in Proceedings of the European Conference on Circuit Theory and Design , Romania, vol. 1, pp. 315-318, 2005.

[10] Cristianini N. and Shawe-Taylor J., An Introduction to Support Vector Machines , Cambridge University Press, UK, 2000.

[11] Deng Y., Li D., Xie X., Lam K., and Dai Q., Partially Occluded Face Completion and Recognition, in Proceedings of the 16 th Image Processing IEEE International Conference on Image Processing , Cairo, pp. 4145-4148, 2008.

[12] Faaya T. and Toygar O., Illumination Invariant Face Recognition under Various Facial Expressions and Occlusions, in Proceedings of the 3 rd International Conference on Image and Signal Processing , Berlin, vol. 5099, pp. 304- 311, 2008.

[13] Farb G., Delmas P., Morris J., and Shorin A., Robust Face Matching under Large Occlusions, in Proceedings of the 14 th International Conference on Image Analysis and Processing , Modena, pp. 448-453, 2007.

[14] Florent P., Jean-Luc D., and Kenneth R., A Probabilistic Model of Face Mapping with Local Transformations and its Application to Person 8 T he International Arab Journal of Information Techno logy, Vol. 11, No. 1, January 2014 Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 27, no. 7, pp. 1157-1171, 2005.

[15] Gundimada S. and Asari V., Facial Recognition using Multi Sensor Images Based on Localized Kernel Eigen Spaces, IEEE Transactions on Image Processing , vol. 18, no. 6, pp. 1314-1325, 2009.

[16] Guo G., Li S., and Chan K., Face Recognition by Support Vector Machines, in Proceedings of the 4 th IEEE International Conference on Automatic Face and Gesture Recognition , Grenoble, pp. 196-201, 2000.

[17] Guoliang L., Kudo M., and Toyama J., Robust Human Pose Estimation from Corrupted Images with Partial Occlusions and Noise Pollutions, in Proceedings of IEEE International Conference on Granular Computing , Japan, pp. 433-438, 2011.

[18] Hastie T., Tibshirani R., and Buja A., Flexible Discriminant Analysis by Optimal Scoring, Journal of the American Statistical Association , vol. 89, no. 428, pp. 1255-1270, 1994.

[19] Hotta K., A View-Invariant Face Detection Method Based on Local PCA Cells, Journal of Advanced Computational Intelligence and Intelligent Informatics , vol. 8, no. 2, pp. 130-139, 2004.

[20] Hotta K., Robust Face Recognition under Partial Occlusion Based on Support Vector Machine with Local Gaussian Summation Kernel, Image and Vision Computing , vol. 26, no. 11, pp. 1490- 1498, 2008.

[21] Jia H. and Martinez A., Face Recognition with Occlusions in the Training and Testing Sets, in Proceedings of the 8 th IEEE International Conference on Automatic Face & Gesture Recognition , Amsterdam, pp. 1-6, 2008.

[22] Jia H. and Martinez A., Support Vector Machines in Face Recognition with Occlusions, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition , Miami, pp. 136- 141, 2009.

[23] Kepenekci B., Tek F., and Akar G., Occluded Face Recognition by using Gabor Features, in Proceedings of the 3 rd COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication , Budapest, pp. 1-6, 2002.

[24] Kim J., Choi J., Yi J., and Turk M., Effective Representation using ICA for Face Recognition Robust to Local Distortion and Partial Occlusion, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 27, no. 12, 2005.

[25] Kim T., Lee K., Lee S., and Yim C., Occlusion Invariant Face Recognition using Two- Dimensional PCA, in Proceedings of International Conferences Advances in Computer Graphics and Computer Vision , Berlin, pp. 305- 315, 2007.

[26] Kurita T., Pic M., and Takahashi M., Recognition and Detection of Occluded Faces by a Neural Network Classifier with Recursive Data Reconstruction, in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance , pp. 53-58, 2003.

[27] Lawrence S., Giles C., Tsoi A., and Back A., Face Recognition: A Convolutional Neural- Network Approach, IEEE Transactions Neural Network , vol. 8, no. 1, pp. 98-113, 1997.

[28] Lee P., Wang Y., Yang M., Hsu J., and Hung Y., Distinctive Personal Traits for Face Recognition under Occlusion, IEEE Conference on Systems, Man and Cybernetics , Taipei, vol. 5, pp. 4202- 4207, 2006.

[29] Lin J., Ming J., and Crookes D., A Probabilistic Union Approach to Robust Face Recognition with Partial Distortion and Occlusion, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing , Las Vegas, pp. 993-996, 2008.

[30] Lin J., Ming J., and Crookes D., Robust Face Recognition with Partially Occluded Images Based on A Single or a Small Number of Training Samples, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing , Taipei, pp. 881-884, 2009.

[31] Liu Q., Yan W., Lu H., and Ma S., Occlusion Robust Face Recognition with Dynamic Similarity Features, in Proceedings of the 18 th International Conference on Pattern Recognition , Hong Kong, vol. 3, pp. 544-547, 2006

[32] Marsico M., Nappi M., and Riccio D., FARO: Face Recognition Against Occlusions and Expression Variations, IEEE Transactions on System, Man, and Cybernetics-Part A: Systems and Humans , vol. 40, no. 1, pp. 121-132, 2010.

[33] Martinez A. and Benavente R., The AR Face Database, Technical Report , Computer Vision Center, The Ohio State University, 1998.

[34] Martinez A., Recognition of Partially Occluded and/or Imprecisely Localized Faces using a Probabilistic Approach, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition , Hilton Head Island, vol. 1, pp. 712-717, 2000.

[35] Martinez A., Recognizing Imprecisely Localized, Partially Occluded and Expression Variant Faces from a Single Sample Per Class, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 24, no. 6, pp. 748-763, 2002. A Survey: Face Recognition Techniques under Partial Occlusion 9

[36] McCloskey S., Langer M., and Siddiqi K., Removal of Partial Occlusion from Single Images, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 33, no. 3, pp. 647- 654, 2011.

[37] Meng Y., Lei Z., Simon C., and David Z., Gabor Feature Based Robust Representation and Classification for Face Recognition with Gabor Occlusion Dictionary, Pattern Recognition, vol. 46, no. 7, pp. 1865-1878, 2012.

[38] Oh H., Lee K., and Lee S., Occlusion Invariant Face Recognition using Selective Local Non- Negative Matrix Factorization Basis Images, Image and Vision Computing , vol. 26, no. 11, pp. 1515-1523, 2008.

[39] Perlibakas V., Distance Measures for PCA- Based Face Recognition, Pattern Recognition Letters , vol. 25, no. 6, pp. 711-724, 2004.

[40] Phillips P., Flynn P., Scruggs T., Bowyer K., Chang J., Hoffman K., Marques J., Min J., and Worek W., Overview of the Face Recognition Grand Challenge, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition , USA, vol. 1, pp. 947-954, 2005.

[41] Pontil M. and Verri A., Support Vector Machines for 3D Object Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 20, no. 6, pp. 637-646, 1998.

[42] Rama A., Tarres F., Goldmann L., and Sikora T., More Robust Face Recognition by Considering Occlusion Information, in Proceedings of the 8 th IEEE International Conference on Automatic Face & Gesture Recognition , Amsterdam, pp. 1- 6, 2008.

[43] Sharif M., Younas J., and Sajjad M., Face Recognition Based on Facial Features, Research Journal of Applied Sciences, Engineering and Technology , vol. 4, no. 17, pp. 2879-2886, 2012.

[44] Sharif M., Sajjad M., Abdul H., Younas J., and Mudassar R., 3D Face Recognition using Horizontal and Vertical Marked Strips, SINDH University Research Journal , vol. 43, no. 1-A, pp. 57-62, 2011.

[45] Sharif M., Sajjad M., Jawad J., and Mudassar R., Illumination Normalization Preprocessing for Face Recognition, in Proceedings of the 2 nd Conference on Environmental Science and Information Application Technology , China, pp. 44-47, 2010.

[46] Sharif M., Sajjad M., Mughees A., Younas J., and Mudassar R., Using Nose Heuristics for Efficient Face Recognition, SINDH University Research Journal , vol. 43, no. 1-A, pp. 63-68, 2011.

[47] Sharif M., Adeel K., Mudassar R., and Sajjad M., Face Recognition using Gabor Filters, Journal of Applied Computer Science & Mathematics , vol. 5, no. 11, pp. 53-57, 2011.

[48] Sharif M., Sajjad M., Younas J., and Atif A., Single Image Face Recognition using Laplacian of Gaussian and Discrete Cosine Transforms, International Arab Journal of Information Technology , vol. 9, no. 6, pp. 562-570, 2012.

[49] Sharif M., Kamran A., Mudassar R., and Sajjad M., Data Reductionality Technique for Face Recognition, in Proceedings of the Pakistan Academy of Sciences , vol. 48, no. 4, pp. 229-234, 2011.

[50] Sharif M., Sajjad M., Jawad J., Younas J., and Mudassar R., Face Recognition for Disguised Variations using Gabor Feature Extraction, Australian Journal of Basic and Applied Sciences , vol. 5, no. 6, pp. 1648-1656, 2011.

[51] Shermina J. and Vasudevan V., Face Recognition System with Various Expression and Occlusion Based on a Novel Block Matching Algorithm and PCA, International Journal of Computer Applications , vol. 38, no. 11, pp. 27- 34, 2012.

[52] Schneiderman H. and Kanade T., A Statistical Method for 3D Object Detection Applied to Faces and Cars, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition , Pittsburgh, pp. 746-751, 2000.

[53] Struc V., Dobrisek S., and Pavesic N., Confidence Weighted Subspace Projection Techniques for Robust Face Recognition in the Presence of Partial Occlusions, in Proceedings of the 20 th International Conference on Pattern Recognition , Istanbul, pp. 1334-1338, 2010.

[54] Tan X., Chen S., Zhou Z., and Liu J., Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion, in Proceedings of IEEE Computer Society Conference Computer Vision and Pattern Recognition , New York, pp. 138- 145, 2006.

[55] Tan X., Chen S., Zhou Z., and Liu J., Face Recognition under Occlusions and Variant Expressions with Partial Similarity, IEEE Transaction on Information Forensics and Security , vol. 4, no. 2, pp. 217-230, 2009.

[56] Tan X., Chen S., Zhou Z., and Zhang F., Recognizing Partially Occluded, Expression Variant Faces from Single Training Image Per Person with SOM and Soft kNN Ensemble, in Proceedings of IEEE Transaction on Neural Networks , vol. 16, pp. 1-13, 2005.

[57] Tarres F. and Rama A., A Novel Method for Face Recognition under Partial Occlusion or Facial Expression Variations, in Proceedings of the 47 th International Symposium , pp. 163-166, 2005. 10 The International Ar ab Journal of Information Technology, Vol. 11, No. 1, January 2014

[58] Vapnik V., Statistical Learning Theory , John Wiley & Sons, 1998.

[59] Wang X., Han T., and Yan S., An HOG-LBP Human Detector with Partial Occlusion Handling, in Proceedings of IEEE 12 th International Conference on Computer Vision , Kyoto, pp. 32-39, 2009.

[60] Wright J., Yang A., Ganesh A., Sastry S., and Ma Y., Robust Face Recognition via Sparse Representation, IEEE Transaction on Pattern Analysis and Machine Intelligence , vol. 31, no. 2, pp. 210-227, 2009.

[61] Yang M., Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition using Kernel Methods, in Proceedings of the 5 th IEEE International Conference on Automatic Face and Gesture Recognition , USA, vol. 14, pp. 215-220, 2002.

[62] Yan S., Wang H., Liu J., Tang X., and Huang T., Misalignment-Robust Face Recognition, IEEE Transaction on Image Processing , vol. 19, no. 4, pp. 1087-1096, 2010.

[63] Yu X., Tian J., and Liu J., Active Appearance Models Fitting with Occlusion, in Proceedings of the 6 th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition , Berlin, pp. 137-144, 2007.

[64] Zhang W., Shan S., Chen X., and Gao W., Local Gabor Binary Patterns Based on Kullback- Leibler Divergence for Partially Occluded Face Recognition, IEEE Signal Processing Letters , vol. 14, no. 11, pp. 875-878, 2007.

[65] Zhao H. and Yuen P., Incremental Linear Discriminant Analysis for Face Recognition, IEEE Transactions on Systems , Man, and Cybernetics, Part B: Cybernetics , vol. 38, no. 1, pp. 210-221, 2008.

[66] Zhao H., Yuen P., and Kwok J., A Novel Incremental Principal Component Analysis and its Application for Face Recognition, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , vol. 36, no. 4, pp. 873-886, 2006.

[67] Zhao W., Chellappa R., Phillips P., and Rosenfeld A., Face Recognition: A Literature Survey, ACM Computing Surveys , vol. 35, no. 4, pp. 399-458, 2003.

[68] Zhiwei Z. and Qiang J., Robust Pose Invariant Facial Feature Detection and Tracking in Real- Time, in Proceedings of the 18 th International Conference on Pattern Recognition , pp. 1092- 1095, 2006.

[69] Zihan Z., Andrew W., Hossein M., John W., and Yi M., Face Recognition with Contiguous Occlusion Using Markov Random Fields, in Proceedings of IEEE 12 th International Conference on Computer Vision , Kyoto, pp. 1050-1057, 2009.

[70] Zisheng L., Jun-ichi I., and Masahide K., Block- Based Bag of Words for Robust Face Recognition under Variant Conditions of Facial Expression, Illumination, and Partial Occlusion, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences , vol. E94-A, no. 2, pp. 533-541, 2011. Aisha Azeem is a lecturer in University of Wah, Pakistan. She completed her BS and MS degrees from CIIT Wah in 2008 and 2011 respectively. Her research interests include digital image processing and software engineering. Muhammad Sharif has been an assistant professor at Department of Computer Science, COMSATS Institute of Information Technology Pakistan. He holds PhD and has more than 18 years experience of teaching and research. Mudassar Raza is an assistant professor at COMSATS Institute of Information Technology, Pakistan. He has more than seven years of experience of teaching undergraduate classes at CIIT Wah. He has been supervising final year projects to undergraduate students. His interest in clude are digital image processing, and parallel and distributed computing. Marryam Murtaza She is currently a student of MSc in computer science at COMSATS Institute of Information Technology, Pakistan. she is working on her thesis. Her research interests include digital image processing and software engineering. She had completed her BSC in computer science degree from COMSATS Institute of Information Technology Wah in 2008.