BLACKFACE SURVEILLANCE CAMERA DATABASE FOR EVALUATING FACE RECOGNITION IN LOW QUALITY SCENARIOS
DOI:
https://doi.org/10.51406/jnset.v15i2.1668Keywords:
Algorithm, Databases, Face-recognition, Performance, Pose, Quality and Surveillance.Abstract
Many face recognition algorithms perform poorly in real life surveillance scenarios because they were tested with datasets that are already biased with high quality images and certain ethnic or racial types. In this paper a black face surveillance camera (BFSC) database was described, which was collected from four low quality cameras and a professional camera. There were fifty (50) random volunteers and 2,850 images were collected for the frontal mugshot, surveillance (visible light), surveillance (IR night vision), and pose variations datasets, respectively. Images were taken at distance 3.4, 2.4, and 1.4 metres from the camera, while the pose variation images were taken at nine distinct pose angles with an increment of 22.5 degrees to the left and right of the subject. Three Face Recognition Algorithms (FRA), a commercially available Luxand SDK, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were evaluated for performance comparison in low quality scenarios. Results obtained show that camera quality (resolution), face-to-camera distance, average recognition time, lighting conditions and pose variations all affect the performance of FRAs. Luxand SDK, PCA and LDA returned an overall accuracy of 97.5%, 93.8% and 92.9% after categorizing the BFSC images into excellent, good and acceptable quality scales.
References
Abayomi-Alli, A. 2015. Image Verification and Quality Assessment Models for Predicting Facial Recognition System Performance, PhD Computer Science and Engineering Thesis, Ladoke Akintola University of Technology, Nigeria.
Abayomi-Alli, A., Omidiora E. O., Olabiyisi S. O., Ojo J. A. 2015. Adaptive Regression Splines Models for Predicting Facial Image Verification and Quality Assessment Scores, Balkan Journal of Electrical and Computer Engineering (BAJECE). 3(1): 17-26.
Abdallah, A. S., El-Nasr, M. A., Abbott, A. C. 2007. “A New Colour Image Database for Benchmarking of Automatic Face Detection and Human Skin Segmentation Techniques”. Proceedings of World Academy of Science, Engineering and Technology, 20: 353-357.
Amir, F. 2008. Robust Face Detection Using Template Matching Algorithm. Masters of Applied Science thesis, University of Toronto, Canada.
Belhumeur, P. N., Hespanha, J. P. and Kriegman, D. J. 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. on PAMI, 19(7):711-720.
Cai, D., He, X., Han, J. 2007. Semi-supervised discriminant analysis, Proc. IEEE 11th International Conference on Computer Vision, 14:1–7.
Chen, X., Flynn, P. J., and Bowyer, K. W. 2005. IR and visible light face recognition. Computer Vs Image Understanding, 99(3):332–358.
Draper, B., Kyungim, B., Marian, S. B., Ross, J. B. 2003. Computer Vision and Image Understanding: Recognizing faces with PCA and ICA. Retrieved from http://www.elsevier.com/locate/cviu
Du, Y., Chang, C. I. 2007. Rethinking the effective assessment of biometric Systems, SPIE Newsroom, Available online at http://spie.org/x2412.xml, accessed on 13th November, 2014.
FaceRecHomePage 2012. Facial Recognition Homepage, Available online at http://www.face-rec.org/algorithms/new-papers/databases, accessed on 11/05/2013.
Gao, W., Cao, B., Shans, S., Zhou, D., Zhang, X., Zhao, D. 2004. CASP-PEAL Large Scale Chinese Face Database and Evaluation Protocols Technical Report. JDL-TR-04FR-001, Joint research Development laboratory. Available at: http://www.jdl.ac.cn/peal/index.html
Grgic, M., Delac, K., Grgic, S. 2011. SCface-Surveillance Cameras Face Database, Multimed Tools Application, 51:863–879.
Gross, R. 2005.Face database, handbook of face recognition, Eds. Stanz li and Arilk. Jan, Springer–verlag February, 2005.
ISO/IEC 2006. Proposed Draft Amendment to ISO/IEC 19794-5 Face Image Data on Conditions for Taking Pictures, ISO/IEC JTC 1/SC 37 N 1511.
Jain, A. 2008. Biometrics, Microsoft Encarta 2008, Microsoft Corporation, Redmond, WA, 2008.
Jain, V., Mukherjee, A. 2002. The Indian Face Database, Available at http://viswww.is.umass.edu/~vidit/IndianFaceDatabase2002
Luxand 2013. Detect and Recognize Faces with Luxand FaceSDK, Luxand Incorporation, USA, Available online at http://www.luxand.com/facesdk/, Accessed on 23rd August, 2013.
Mansfield, A. J., Wayman, J. L. 2002. Best Practices in Testing and Reporting Performance of Biometric Devices, Centre for Mathematics and Scientific Computing, National Physical Laboratory, Middlesex, TW110LW. Version 2.01 NPL Report CMSC 14/02.
Martine A. M., Benavente, R. 2002. The AR Face Database, CVC Technical Report #24, June 1998.
Omidiora, E. O., Olabiyisi, S. O., Ojo, J. A., Abayomi-Alli, A., Abayomi-Alli, O. , Erameh, K. B. 2013(a). Facial Image Verification and Quality Assessment System FaceIVQA. International Journal of Electrical and Computer Engineering (IJECE), 3(6), 863-874.
Omidiora, E. O., Olabiyisi, S. O., Ojo, J. A., Abayomi-Alli, A.; Izilein, F., Ezomo, P. I. 2013(b). Mace correlation filter algorithm for face verification in surveillance scenario, IJCSE, UK, 18(1):6-15
Omidiora, E. O., Olabiyisi, S. O., Ojo, J. A., Ganiyu, R. A., Abayomi-Alli, A. 2014. Enhanced Face Verification and Image Quality Assessment Scheme Using Modified Optical Flow Technique, Lecture Notes in Engineering and Computer Science: In: Proceedings of the International Conference on Signal Processing and Imaging Engineering (ICSPIE), University of California, Berkeley, San Francisco, USA, 22nd - 24th October, 2014.
Omojola, S. O. 2012. Performance evaluation of facial recognition algorithm in surveillance scenario: a case study of correlation filter, Computer Science B.Sc. Thesis, Federal University of Agriculture, Abeokuta, Nigeria.
ORL 2013. ORL face database, Available online at http://www.uk.research.att.com/facedatabase.html, accessed on 13/09/2013.
Park, U. 2009. Face recognition: face in video, age invariance, and facial marks, Ph.D. Dissertation, Department of Computer Science, Michigan State University, U.S.A.
Phillips, P. J., Moon, H., Rizvi, S. A., Rauss, P. J. 2000. The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Transaction on Pattern Analysis and Machine Intelligence. 22(10):1090-1104.
Poh, N., kittler, J., Marcel, S., Matrouf, D., Bonastre, J. 2010. Model and score adaptation for biometric systems: coping with device interoperability and changing acquisition conditions, In Proc. international Conference on Pattern Recognition, IEEE Computer Society, 23rd - 26th, Istanbul, Turkey, pp. 1229-1232.
Samaria, F., Harter, A. 1994. Parameterization of a Stochastic Model for Human face Identification, 2nd IEEE Workshop on Applications of Computer Vision, 5th-7th December, Saratosa, Florida, pp. 138-142.
Sim, T., Baker, S., Bsat, M. 2003. The CMU Pose, Illumination and Expression Database. IEEE Transactions on Pattern Analysis and Machine Intelligence. 25(12): 1615-1618 2003.
Turk, M., Pentland, A. 1991. Eigenfaces for recognition. J. CognNeurosci 3(1):71–86.
Wallace, R., McLaren, M., McCool, C., Marcel, S. 2011. Inter-session variability modeling and Joint Factor Analysis for Face Authentication, In Proc. International Joint Conference on Biometrics (IJCB), 11th-13th October, Arlington, Virginia, USA, pp. 1-8.
Zhao, W.; Chellappa, R., Rosenfeld, A., Phillips, P. J. 2003. Face recognition: A literature survey. ACM Computing Surveys. 35(4): 399-458.
Abayomi-Alli, A., Omidiora E. O., Olabiyisi S. O., Ojo J. A. 2015. Adaptive Regression Splines Models for Predicting Facial Image Verification and Quality Assessment Scores, Balkan Journal of Electrical and Computer Engineering (BAJECE). 3(1): 17-26.
Abdallah, A. S., El-Nasr, M. A., Abbott, A. C. 2007. “A New Colour Image Database for Benchmarking of Automatic Face Detection and Human Skin Segmentation Techniques”. Proceedings of World Academy of Science, Engineering and Technology, 20: 353-357.
Amir, F. 2008. Robust Face Detection Using Template Matching Algorithm. Masters of Applied Science thesis, University of Toronto, Canada.
Belhumeur, P. N., Hespanha, J. P. and Kriegman, D. J. 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. on PAMI, 19(7):711-720.
Cai, D., He, X., Han, J. 2007. Semi-supervised discriminant analysis, Proc. IEEE 11th International Conference on Computer Vision, 14:1–7.
Chen, X., Flynn, P. J., and Bowyer, K. W. 2005. IR and visible light face recognition. Computer Vs Image Understanding, 99(3):332–358.
Draper, B., Kyungim, B., Marian, S. B., Ross, J. B. 2003. Computer Vision and Image Understanding: Recognizing faces with PCA and ICA. Retrieved from http://www.elsevier.com/locate/cviu
Du, Y., Chang, C. I. 2007. Rethinking the effective assessment of biometric Systems, SPIE Newsroom, Available online at http://spie.org/x2412.xml, accessed on 13th November, 2014.
FaceRecHomePage 2012. Facial Recognition Homepage, Available online at http://www.face-rec.org/algorithms/new-papers/databases, accessed on 11/05/2013.
Gao, W., Cao, B., Shans, S., Zhou, D., Zhang, X., Zhao, D. 2004. CASP-PEAL Large Scale Chinese Face Database and Evaluation Protocols Technical Report. JDL-TR-04FR-001, Joint research Development laboratory. Available at: http://www.jdl.ac.cn/peal/index.html
Grgic, M., Delac, K., Grgic, S. 2011. SCface-Surveillance Cameras Face Database, Multimed Tools Application, 51:863–879.
Gross, R. 2005.Face database, handbook of face recognition, Eds. Stanz li and Arilk. Jan, Springer–verlag February, 2005.
ISO/IEC 2006. Proposed Draft Amendment to ISO/IEC 19794-5 Face Image Data on Conditions for Taking Pictures, ISO/IEC JTC 1/SC 37 N 1511.
Jain, A. 2008. Biometrics, Microsoft Encarta 2008, Microsoft Corporation, Redmond, WA, 2008.
Jain, V., Mukherjee, A. 2002. The Indian Face Database, Available at http://viswww.is.umass.edu/~vidit/IndianFaceDatabase2002
Luxand 2013. Detect and Recognize Faces with Luxand FaceSDK, Luxand Incorporation, USA, Available online at http://www.luxand.com/facesdk/, Accessed on 23rd August, 2013.
Mansfield, A. J., Wayman, J. L. 2002. Best Practices in Testing and Reporting Performance of Biometric Devices, Centre for Mathematics and Scientific Computing, National Physical Laboratory, Middlesex, TW110LW. Version 2.01 NPL Report CMSC 14/02.
Martine A. M., Benavente, R. 2002. The AR Face Database, CVC Technical Report #24, June 1998.
Omidiora, E. O., Olabiyisi, S. O., Ojo, J. A., Abayomi-Alli, A., Abayomi-Alli, O. , Erameh, K. B. 2013(a). Facial Image Verification and Quality Assessment System FaceIVQA. International Journal of Electrical and Computer Engineering (IJECE), 3(6), 863-874.
Omidiora, E. O., Olabiyisi, S. O., Ojo, J. A., Abayomi-Alli, A.; Izilein, F., Ezomo, P. I. 2013(b). Mace correlation filter algorithm for face verification in surveillance scenario, IJCSE, UK, 18(1):6-15
Omidiora, E. O., Olabiyisi, S. O., Ojo, J. A., Ganiyu, R. A., Abayomi-Alli, A. 2014. Enhanced Face Verification and Image Quality Assessment Scheme Using Modified Optical Flow Technique, Lecture Notes in Engineering and Computer Science: In: Proceedings of the International Conference on Signal Processing and Imaging Engineering (ICSPIE), University of California, Berkeley, San Francisco, USA, 22nd - 24th October, 2014.
Omojola, S. O. 2012. Performance evaluation of facial recognition algorithm in surveillance scenario: a case study of correlation filter, Computer Science B.Sc. Thesis, Federal University of Agriculture, Abeokuta, Nigeria.
ORL 2013. ORL face database, Available online at http://www.uk.research.att.com/facedatabase.html, accessed on 13/09/2013.
Park, U. 2009. Face recognition: face in video, age invariance, and facial marks, Ph.D. Dissertation, Department of Computer Science, Michigan State University, U.S.A.
Phillips, P. J., Moon, H., Rizvi, S. A., Rauss, P. J. 2000. The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Transaction on Pattern Analysis and Machine Intelligence. 22(10):1090-1104.
Poh, N., kittler, J., Marcel, S., Matrouf, D., Bonastre, J. 2010. Model and score adaptation for biometric systems: coping with device interoperability and changing acquisition conditions, In Proc. international Conference on Pattern Recognition, IEEE Computer Society, 23rd - 26th, Istanbul, Turkey, pp. 1229-1232.
Samaria, F., Harter, A. 1994. Parameterization of a Stochastic Model for Human face Identification, 2nd IEEE Workshop on Applications of Computer Vision, 5th-7th December, Saratosa, Florida, pp. 138-142.
Sim, T., Baker, S., Bsat, M. 2003. The CMU Pose, Illumination and Expression Database. IEEE Transactions on Pattern Analysis and Machine Intelligence. 25(12): 1615-1618 2003.
Turk, M., Pentland, A. 1991. Eigenfaces for recognition. J. CognNeurosci 3(1):71–86.
Wallace, R., McLaren, M., McCool, C., Marcel, S. 2011. Inter-session variability modeling and Joint Factor Analysis for Face Authentication, In Proc. International Joint Conference on Biometrics (IJCB), 11th-13th October, Arlington, Virginia, USA, pp. 1-8.
Zhao, W.; Chellappa, R., Rosenfeld, A., Phillips, P. J. 2003. Face recognition: A literature survey. ACM Computing Surveys. 35(4): 399-458.
