Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification

详细信息    查看全文

• 作者：Bappaditya Mandal (15)
  Wang Zhikai (16)
  Liyuan Li (15)
  Ashraf A. Kassim (16)
  
  15. Visual Computing Department ; Institute for Infocomm Research ; Singapore ; Singapore
  16. Electrical and Computer Engineering ; National University of Singapore ; Singapore ; Singapore
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9008
• 期：1
• 页码：585-599
• 全文大小：857 KB
• 参考文献：1. GoPro (2014). http://gopro.com/
  2. Google: Google glass (2014). http://www.google.com/glass/start/
  3. Mandal, B., Eng., H.L.: 3-parameter based eigenfeature regularization for human activity recognition. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 954鈥?57 (2010)
  4. TedBlog: The future of facial recognition: 7 fascinating facts. http://blog.ted.com/2013/10/17/the-future-of-facial-recognition-7-fascinating-facts/ 2014
  5. Mandal, B, Eng, HL (2012) Regularized discriminant analysis for holistic human activity recognition. IEEE Intell. Syst. 27: pp. 21-31 CrossRef
  6. Zhao, W, Chellappa, R, Phillips, PJ, Rosenfeld, A (2003) Face recognition: a literature survey. ACM Comput. Surv. 35: pp. 399-458 CrossRef
  7. Phillips, P.J.: Face & ocular challenges (2010). Presentation: http://www.cse.nd.edu/BTAS_10/BTAS_Jonathon_Phillips_Sep_2010_FINAL.pdf
  8. Grother, P., Ngan, M.: Face recognition vendor test (frvt) performance of face identification algorithms (2014). Technical Report: http://biometrics.nist.gov/cs_links/face/frvt/frvt2013/NIST_8009.pdf
  9. Mandal, B., Jiang, X.D., Kot, A.: Multi-scale feature extraction for face recognition. In: IEEE International Conference on Industrial Electronics and Applications (ICIEA), pp. 1鈥? (2006)
  10. Wang, X., Zhao, X., Prakash, V., Shi, W., Gnawali, O.: Computerized-eyewear based face recognition system for improving social lives of prosopagnosics. In: Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare, pp. 77鈥?0 (2013)
  11. Mandal, B., Ching, S., Li, L., Chandrasekha, V., Tan, C., Lim, J.H.: A wearable face recognition system on google glass for assisting social interactions. In: 3rd International Workshop on Intelligent Mobile and Egocentric Vision, ACCV (2014)
  12. Utsumi, Y., Kato, Y., Kunze, K., Iwamura, M., Kise, K.: Who are you?: A wearable face recognition system to support human memory. In: ACM Proceedings of the 4th Augmented Human International Conference, pp. 150鈥?53 (2013)
  13. Krishna, S., Little, G., Black, J., Panchanathan, S.: A wearable face recognition system for individuals with visual impairments. In: ACM SIGACCESS Conference on Computer and Accessbility, pp. 106鈥?13 (2005)
  14. Turk, M, Pentland, A (1991) Eigenfaces for recognition. J. Cogn. Neurosci. 3: pp. 71-86 1991.3.1.71" target="_blank" title="It opens in new window">CrossRef
  15. Swets, DL, Weng, J (1996) Using discriminant eigenfeatures for image retrieval. IEEE PAMI 18: pp. 831-836 CrossRef
  16. Moghaddam, B, Jebara, T, Pentland, A (2000) Bayesian face recognition. Pattern Recogn. 33: pp. 1771-1782 CrossRef
  17. Singletary, B.A., Starner, T.E.: Symbiotic interfaces for wearable face recognition. In: HCII2001 Workshop On Wearable Computing (2001)
  18. Mikolajczyk, K, Schmid, C (2005) A performance evaluation of local descriptors. IEEE PAMI 27: pp. 1615-1630 CrossRef
  19. Ahonen, T, Hadid, A, Pietikainen, M (2006) Face description with local binary patterns: application to face recognition. IEEE PAMI 28: pp. 2037-2041 CrossRef
  20. Aly, M.: Face recognition using sift features. CNS/Bi/EE report 186 (2006)
  21. Ojansivu, V, Heikkil盲, J Blur insensitive texture classification using local phase quantization. In: Elmoataz, A, Lezoray, O, Nouboud, F, Mammass, D eds. (2008) Image and Signal Processing. Springer, Heidelberg, pp. 236-243 CrossRef
  22. Kannala, J., Rahtu, E.: Bsif: Binarized statistical image features. In: ICPR, pp. 1363鈥?366 (2012)
  23. Perlibakas, V (2004) Distance measures for pca-based face recognition. Pattern Recogn. Lett. 25: pp. 711-724 CrossRef
  24. Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification (2010)
  25. Phillips, PJ, Moon, H, Rizvi, S, Rauss, P (2000) The feret evaluation methodology for face recognition algorithms. IEEE PAMI 22: pp. 1090-1104 CrossRef
  26. Martinez, AM (2002) Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE PAMI 24: pp. 748-763 CrossRef
  27. Nefian, A.V.: Georgia tech face database (2014). http://www.anefian.com/research/face_reco.htm
  28. Mandal, B., Ching, S., Li, L.: Werable device database (2014). andal/human-detection-and-fr" class="a-plus-plus">https://sites.google.com/site/bappadityamandal/human-detection-and-fr
  29. Beveridge, R., Bolme, D., Teixeira, M., Draper, B.: The csu face identification evaluation system users guide: Version 5.0 (2013). Technical Report: http://www.cs.colostate.edu/evalfacerec/data/normalization.html
  30. Jiang, XD, Mandal, B, Kot, A (2008) Eigenfeature regularization and extraction in face recognition. IEEE PAMI 30: pp. 383-394 CrossRef
  31. Jiang, X.D., Mandal, B., Kot, A.: Face recognition based on discriminant evaluation in the whole space. In: IEEE 32nd International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), Honolulu, Hawaii, USA, pp. 245鈥?48 (2007)
  32. Mandal, B, Jiang, X, Eng, HL, Kot, A (2010) Prediction of eigenvalues and regularization of eigenfeatures for human face verification. Pattern Recogn. Lett. 31: pp. 717-724 CrossRef
  33. Mandal, B., Jiang, X.D., Kot, A.: Dimensionality reduction in subspace face recognition. In: IEEE ICICS, pp. 1鈥? (2007)
  34. VLFEAT: Vlfeat open source (2014). http://www.vlfeat.org/overview/sift.html#tut.sift.param
  35. Lu, J, Plataniotis, KN, Venetsanopoulos, AN, Li, SZ (2006) Ensemble-based discriminant learning with boosting for face recognition. IEEE TNN 17: pp. 166-178
  36. Jiang, XD, Mandal, B, Kot, A (2009) Complete discriminant evaluation and feature extraction in kernel space for face recognition. Mach. Vis. Appl. 20: pp. 35-46 CrossRef
  37. Park, BG, Lee, KM, Lee, SU (2005) Face recognition using face-arg matching. IEEE Trans. Pattern Anal. Mach. Intell. 27: pp. 1982-1988 CrossRef
  38. Geng, C, Jiang, X (2013) Fully automatic face recognition framework based on local and global features. Mach. Vis. Appl. 24: pp. 537-549 CrossRef
  39. Mandal, B., Jiang, X.D., Kot, A.: Verification of human faces using predicted eigenvalues. In: 19th International Conference on Pattern Recognition (ICPR), Tempa, Florida, USA (2008)
  40. Viola, P, Jones, M (2004) Robust real-time face detection. IJCV 57: pp. 137-154 CrossRef
  41. Yu, X, Han, W, Li, L, Shi, JY, Wang, G An eye detection and localization system for natural human and robot interaction without face detection. In: Gro脽, R, Alboul, L, Melhuish, C, Witkowski, M, Prescott, TJ, Penders, J eds. (2011) Towards Autonomous Robotic Systems. Springer, Heidelberg, pp. 54-65 CrossRef
  
• 作者单位：Computer Vision - ACCV 2014 Workshops
• 丛书名：978-3-319-16627-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Face identification (FI) has made significant amount of progress in the last three decades. Its application is now moving towards wearable devices (like Google Glass and mobile devices) leading to the problem of FI on first-person-views (FPV) or ego-centric videos for scenarios like business networking, memory assistance, etc. In the existing literature, performance analysis of various image descriptors on FPV data are little known. In this paper, we evaluate four popular image descriptors: local binary patterns (LBP), scale invariant feature transform (SIFT), local phase quantization (LPQ) and binarized statistical image features (BSIF) and ten different distance measures: Euclidean, Cosine, Chi square, Spearman, Cityblock, Minkowski, Correlation, Hamming, Jaccard and Chebychev with first nearest neighbor (1-NN) and support vector machines (SVM) as classifiers for FI task on both benchmark databases: FERET, AR, GT and FPV database collected using wearable devices like Google Glass (GG). Comparative analysis on these databases using various descriptors shows the superiority of BSIF with Cosine, Chi square and Cityblock distance measures using 1-NN as classifier over other descriptors and distance measures and even some of the current state-of-art benchmark database results.